diff --git a/.gitignore b/.gitignore
index 197b83f41..f98ce4b2f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -52,3 +52,12 @@ otel-app/otel-app
 deploy/scripts/__pycache__/
 *.pyc
 deploy/eval-results/
+
+# Compiled eval-driver binaries (built by the f2 eval scripts into their cmd dirs)
+asap-precompute-go/monitor/grpcclient/f2driver
+asap-precompute-go/monitor/grpcclient/e2edriver
+asap-precompute-go/monitor/grpcclient/cmd/*/f2driver
+asap-precompute-go/monitor/grpcclient/cmd/*/e2edriver
+
+# Local dataset working copies (not part of the repo)
+datasets_eval/debs/data/
diff --git a/datasets_eval/debs/scripts/debs_backend_accuracy.sh b/datasets_eval/debs/scripts/debs_backend_accuracy.sh
new file mode 100755
index 000000000..e4d0ef7ce
--- /dev/null
+++ b/datasets_eval/debs/scripts/debs_backend_accuracy.sh
@@ -0,0 +1,121 @@
+#!/usr/bin/env bash
+# Real-dataset QUERY ACCURACY through the REAL ASAPQuery-backend, on DEBS 2022.
+# Maps DEBS trading events → OTLP for the four sketch families, replays them
+# through a fused asap_edge agent into data_plane, then QUERIES the warm backend
+# and scores each answer against the EXACT ground truth computed offline from the
+# same DEBS rows:
+#   quantile (DDSketch+KLL) · topk (CountSketch) · distinct (HLL) · freq (CountMin)
+#
+# Uses the multisketch single-host stack.sh (agent + data_plane + control_plane),
+# the same real backend the cluster runs. Usage: debs_backend_accuracy.sh [n_events]
+set -uo pipefail
+ROOT=/mydata/ASAPCollector
+MS="${ROOT}/datasets_eval/multisketch"
+DEBS_CSV="${DEBS_CSV:-${ROOT}/datasets_eval/debs/data/debs2022-gc-trading-day-08-11-21.csv}"
+# N events. 500k DEBS events ≈ 1.55M OTLP points, which SEND in ~23s — well
+# within one boundary-aligned 60s window (measured). The wall-clock-anchor
+# collapses every row into ONE window; boundary alignment (below) gives the send
+# a full 60s of headroom so nothing seals mid-replay. A smaller slice (e.g. 60k)
+# is the flat intro of the trace — no heavy hitters, half the prices zero — so
+# keep the rich 500k slice; it fits.
+N="${1:-500000}"
+JSONL=/tmp/debs_otlp.jsonl; GT=/tmp/debs_gt.json
+BASE="http://127.0.0.1:9091"
+trap '[ -n "${KEEP_STACK:-}" ] || "${MS}/stack.sh" down >/dev/null 2>&1 || true' EXIT
+
+echo "== 1. map DEBS ($N events) → OTLP + exact GT =="
+python3 "${ROOT}/datasets_eval/debs/scripts/debs_otlp_map.py" "$DEBS_CSV" "$N" "$JSONL" "$GT"
+
+echo "== 2. bring up the real all-families stack (agent + data_plane + control_plane) =="
+AGENT_CFG="${AGENT_CFG:-${MS}/agent-allfamilies-coldoff.yaml}"
+echo "   agent config: ${AGENT_CFG}"
+"${MS}/stack.sh" up "${MS}/workloads/all-families.yaml" "${AGENT_CFG}" >/tmp/debs_stack.log 2>&1
+sleep 8
+
+echo "== 3. replay DEBS OTLP → agent :4317 (wall-clock-anchored) =="
+# Align to a fresh 60s wall-clock boundary so the collapsed instant sits at the
+# START of a window — the whole send then has a full 60s of headroom before the
+# window seals (otherwise a mid-window anchor can straddle the seal and drop the
+# tail of the replay).
+python3 -c 'import time; t=time.time(); s=60-(t%60); s=s if s>5 else s+60; print(f"   aligning to next 60s boundary in {s:.1f}s"); time.sleep(s)'
+# --anchor-span-s 50: spread the points across 50s (distinct ns each) inside the
+# one window, so the count-type sketches (topk_cs/freq_cms carry value==1.0 per
+# event) keep per-key multiplicity instead of deduping every event of a key onto
+# one identical (series, ts, value) sample.
+python3 "${ROOT}/datasets_eval/google_cluster/run.py" replay \
+  --jsonl "$JSONL" --endpoint 127.0.0.1:4317 --pace-factor 0 --wall-clock-anchor \
+  --anchor-span-s 45 \
+  >/tmp/debs_replay.log 2>&1
+echo "   waiting for the warm window to seal…"; sleep 75
+
+echo "== 4. query the backend + score vs exact GT =="
+python3 - "$BASE" "$GT" <<'PY'
+import sys, json, time, urllib.request, urllib.parse
+BASE, GT = sys.argv[1], sys.argv[2]
+gt = json.load(open(GT))
+def q(promql, t=None):
+    p = {"query": promql}
+    if t is not None: p["time"] = t
+    u = f"{BASE}/api/v1/query?" + urllib.parse.urlencode(p)
+    try:
+        body = json.loads(urllib.request.urlopen(u, timeout=60).read().decode())
+        if isinstance(body, dict):
+            return body.get("data", {}).get("result", []) or []
+    except Exception:
+        pass
+    return []
+def val(res):
+    try:
+        return float(res[0]["value"][1]) if res else None
+    except Exception:
+        return None
+# Instant sketch reads only resolve against the window whose seal is 'current' —
+# after it rolls the same query goes empty. Sweep time= backward to the offset
+# that returns the MOST series, i.e. the sealed replay window.
+def best_time(probe):
+    now = int(time.time()); best_t, best_n = now, -1
+    for off in range(0, 200, 8):
+        n = len(q(probe, t=now-off))
+        if n > best_n: best_n, best_t = n, now-off
+    return best_t, best_n
+# Probe with the instant topk aggregate (the bare metric never returns series).
+bt, bn = best_time('topk(50, google_cluster_2019_cpu_rate_topk_cs)')
+print(f"# sealed-window probe: t=now-{int(time.time())-bt}s has {bn} topk_cs series")
+# landed-event sanity: total inserts the CountSketch actually saw this window.
+tot = val(q('sum(count_over_time(google_cluster_2019_cpu_rate_topk_cs[300s]))', t=bt))
+print(f"# CountSketch landed events this window: {tot}  (GT events sent: {gt['events']})")
+
+print(f"\n{'query':<26} {'backend':>14} {'exact GT':>14} {'accuracy':>22}")
+print("-"*80)
+
+# distinct (HLL) — cardinality of distinct symbols
+r = q('count(google_cluster_2019_cpu_rate_card_hll)', t=bt)
+est = val(r); tru = gt["distinct"]
+acc = f"rel_err {abs(est-tru)/tru:.2%}" if est else f"({r})"
+print(f"{'distinct (HLL)':<26} {str(est):>14} {tru:>14} {acc:>22}")
+
+# frequency of one key — from the CountSketch heap (point-frequency sketch).
+sym = gt["freq_query"]["symbol"]; tru = gt["freq_query"]["true_count"]
+est = val(q(f'google_cluster_2019_cpu_rate_topk_cs{{item="{sym}"}}', t=bt))
+acc = f"rel_err {abs(est-tru)/tru:.2%}" if est else f"(not in heap)"
+print(f"{'freq '+sym[:16]+' (CS)':<26} {str(est):>14} {tru:>14} {acc:>22}")
+
+# topk (CountSketch) — recall@10 + dump the actual returned items/counts.
+r = q('topk(10, google_cluster_2019_cpu_rate_topk_cs)', t=bt)
+got_items = [(s["metric"].get("item"), s["value"][1]) for s in r]
+got = {i for i, _ in got_items if i}
+truk = {d["symbol"] for d in gt["topk10"]}
+rec = len(got & truk) / len(truk) if truk else 0
+print(f"{'topk@10 (CountSketch)':<26} {str(len(got & truk))+'/'+str(len(truk)):>14} {str(len(truk)):>14} {'recall '+f'{rec:.0%}':>22}")
+print(f"   backend top: {got_items[:5]}")
+print(f"   GT      top: {[(d['symbol'], d['count']) for d in gt['topk10'][:5]]}")
+
+# quantile (DDSketch, KLL) — dump p50/p90/p99 to see if it's a scale/tail issue.
+for fam, m in [("DDSketch", "q_ddsketch"), ("KLL", "q_kll")]:
+    for ql, key in [(0.50, "p50"), (0.90, "p90"), (0.99, "p99")]:
+        est = val(q(f'quantile_over_time({ql}, google_cluster_2019_cpu_rate_{m}[300s])', t=bt))
+        tru = gt["quantile"][key]
+        acc = f"rel_err {abs(est-tru)/tru:.2%}" if est and tru else f"(empty)"
+        print(f"{fam+' '+key:<26} {str(round(est,2) if est else est):>14} {str(tru):>14} {acc:>22}")
+PY
+echo "== done =="
diff --git a/datasets_eval/debs/scripts/debs_otlp_map.py b/datasets_eval/debs/scripts/debs_otlp_map.py
new file mode 100644
index 000000000..a27979439
--- /dev/null
+++ b/datasets_eval/debs/scripts/debs_otlp_map.py
@@ -0,0 +1,93 @@
+#!/usr/bin/env python3
+"""Map real DEBS 2022 trading events → an OTLP-replay JSONL that exercises the
+asap agent's four sketch families, and compute the EXACT ground truth for each
+query so the backend answer can be scored:
+
+  metric (agent family)              query           GT
+  ---------------------------------  --------------  --------------------------
+  top_endpoint_qps    (CountSketch)  topk symbols    exact top-10 by trade count
+  endpoint_request_freq (CountMin)   freq of a key   exact count of one symbol
+  unique_users_per_min (HLL)         distinct count  exact distinct symbols
+  http_requests_total_latency_ms(KLL) quantile       exact p50/p90/p99 of Ask price
+
+JSONL line: {"timestamp_ms","series_id","metric_name","value","attributes":{}}
+Usage: debs_otlp_map.py <csv> <n_events> <out.jsonl> <gt.json>
+"""
+import sys, json, time
+from collections import Counter
+
+csv, n, out_jsonl, gt_path = sys.argv[1], int(sys.argv[2]), sys.argv[3], sys.argv[4]
+
+counts = Counter()      # symbol -> trade count (topk / frequency GT)
+prices = []             # Ask prices (quantile GT)
+distinct = set()
+now_ms = int(time.time() * 1000)
+
+read = 0
+with open(csv, errors="replace") as f, open(out_jsonl, "w") as w:
+    for line in f:
+        if line[0] == "#" or line.startswith("ID,"):
+            continue
+        p = line.split(",")
+        sym = p[0]
+        if not sym:
+            continue
+        ts = now_ms + read  # monotone, wall-clock-anchored downstream
+        # Metric names + labels match agent-allfamilies-coldoff.yaml so the
+        # EXISTING all-families stack serves them (topk item_label=host,
+        # cms/hll item_label=service).
+        # topk (CountSketch) + frequency (CountMin): one point per event, symbol.
+        w.write(json.dumps({"timestamp_ms": ts, "series_id": f"tk:{sym}",
+                            "metric": "google_cluster_2019_cpu_rate_topk_cs", "value": 1.0,
+                            "attributes": {"host": sym}}) + "\n")
+        w.write(json.dumps({"timestamp_ms": ts, "series_id": f"fq:{sym}",
+                            "metric": "google_cluster_2019_cpu_rate_freq_cms", "value": 1.0,
+                            "attributes": {"service": sym}}) + "\n")
+        # distinct (HLL): distinct label = symbol.
+        w.write(json.dumps({"timestamp_ms": ts, "series_id": f"hll:{sym}",
+                            "metric": "google_cluster_2019_cpu_rate_card_hll", "value": 1.0,
+                            "attributes": {"service": sym}}) + "\n")
+        counts[sym] += 1
+        distinct.add(sym)
+        # quantile (DDSketch + KLL): Ask price (col 5) when present.
+        # ALL prices fold into ONE global sketch (constant item) so the backend
+        # quantile query returns a single global p99 comparable to the global GT.
+        # (Per-symbol DDSketch would need per-symbol GT; a single global quantile
+        # is the clean headline number the user asked for.)
+        if len(p) > 4 and p[4]:
+            try:
+                v = float(p[4])
+                for m in ("google_cluster_2019_cpu_rate_q_ddsketch",
+                          "google_cluster_2019_cpu_rate_q_kll"):
+                    w.write(json.dumps({"timestamp_ms": ts, "series_id": "px:ALL",
+                                        "metric": m, "value": v,
+                                        "attributes": {"host": "ALL"}}) + "\n")
+                prices.append(v)
+            except ValueError:
+                pass
+        read += 1
+        if read >= n:
+            break
+
+prices.sort()
+def pq(q):
+    return prices[min(len(prices) - 1, int(q * len(prices)))] if prices else None
+top10 = counts.most_common(10)
+# Point-frequency query is answered from the CountSketch heap (topk_cs), which
+# retains the top heap_size=100 keys with their estimated counts — so pick a key
+# comfortably INSIDE the heap (rank ~15) rather than at its edge. (The CountMin
+# keyed frequency path is a warm-only safe-miss by design — Phase 2b unfinished —
+# so in the cold-OFF stack a CMS point query returns no result; the CountSketch
+# is itself a point-frequency sketch and answers the same question.)
+freq_key, freq_true = counts.most_common(20)[-1] if len(counts) > 20 else top10[0]
+gt = {
+    "events": read,
+    "topk10": [{"symbol": s, "count": c} for s, c in top10],
+    "freq_query": {"symbol": freq_key, "true_count": freq_true},
+    "distinct": len(distinct),
+    "quantile": {"p50": pq(0.50), "p90": pq(0.90), "p99": pq(0.99), "n": len(prices)},
+}
+json.dump(gt, open(gt_path, "w"), indent=1)
+print(f"# DEBS OTLP map: events={read} distinct={len(distinct)} prices={len(prices)}", file=sys.stderr)
+print(f"# top symbol: {top10[0]}  freq_query symbol: {freq_key}(true={freq_true})", file=sys.stderr)
+print(f"# price quantiles p50={pq(0.5)} p90={pq(0.9)} p99={pq(0.99)}", file=sys.stderr)
diff --git a/datasets_eval/google_cluster/e2e/INTEGRATED_SWEEP_RESULTS.md b/datasets_eval/google_cluster/e2e/INTEGRATED_SWEEP_RESULTS.md
new file mode 100644
index 000000000..e12bad4c9
--- /dev/null
+++ b/datasets_eval/google_cluster/e2e/INTEGRATED_SWEEP_RESULTS.md
@@ -0,0 +1,30 @@
+# Integrated ε-sweep — end-to-end metrics under coordinated sampling
+
+`epsilon_e2e_sweep.py` extends `pareto_sweep.py` to emit **6 metric classes in one
+run**, swept over the admission p (= the ε-floor `p=1/(1+ε²·rate)` the autonomous
+coordinator sets for the corresponding ε): query **accuracy**, query **latency**
+(p50/p99), data **freshness** (sample-generation → backend-queryable), and
+per-process **CPU / RSS / wire bandwidth**.
+
+## Phase-1 (single-node, google-cluster-2019 cpu_rate, pooled, GT p99=0.0494)
+
+| arm | ε | p | accuracy | lat p99 | freshness | bk RSS | pr RSS | cpu cores | wire kbps |
+|---|---|---|---|---|---|---|---|---|---|
+| raw    | 0     | 1.00 | 1.000  | 2.5 ms | —     | 47 MB | 71 MB | 0.115 | 14.7 |
+| dd     | 0     | 1.00 | 0.867* | 2.6 ms | 19 ms | 15 MB | 60 MB | 0.100 | 0.4 |
+| dd     | 0.005 | 0.50 | 0.867  | 2.6 ms | 19 ms | 15 MB | 60 MB | 0.099 | 0.4 |
+| dd     | 0.008 | 0.25 | 0.850  | 2.6 ms | 19 ms | 15 MB | 58 MB | 0.091 | 0.4 |
+| dd     | 0.014 | 0.10 | 0.850  | 2.6 ms | 19 ms | 15 MB | 55 MB | 0.081 | 0.3 |
+| dd     | 0.020 | 0.05 | 0.850  | 2.6 ms | 20 ms | 16 MB | 51 MB | 0.093 | 0.3 |
+
+**Takeaways:** sampling to p=0.05 keeps accuracy flat (high-N pooled is
+sampling-robust), drops edge CPU 0.10→0.08, keeps wire ~40× below raw, backend RSS
+bounded ~15 MB. Warm latency ~2.6 ms (vs cold-tier ~51 ms, Fig 7 / #501).
+
+**Caveats (single-node):** (1) `*`accuracy ~13% low — the regenerated `[30s]`
+window reads a sub-pool; the *calibrated* accuracy-vs-p is in
+`docs/evaluation-plan-figures.md` Fig 3. (2) freshness ~19 ms is a single-node
+artifact — the **compressed** replay (31 d → ~20 s) seals windows mid-run, so
+emit→queryable collapses to ~0. Real freshness + per-component (edge/dp/cp) +
+cold-tier routing are the **Phase-2 cluster run** (wall-clock-paced,
+`measure_freshness.sh` + the probe; per-container `snapshot_resources.sh`).
diff --git a/datasets_eval/google_cluster/e2e/epsilon_e2e_sweep.py b/datasets_eval/google_cluster/e2e/epsilon_e2e_sweep.py
new file mode 100644
index 000000000..ab3a20cc3
--- /dev/null
+++ b/datasets_eval/google_cluster/e2e/epsilon_e2e_sweep.py
@@ -0,0 +1,487 @@
+#!/usr/bin/env python3
+"""Headline accuracy-vs-cost Pareto sweep (Fig 1 of docs/evaluation-plan-figures.md).
+
+Runs ONE consistent sweep on the real ASAP stack against the 2019 Google
+cluster trace, producing operating points (cost, accuracy) where:
+
+  y = accuracy = 1 - p99_rel_err of quantile_over_time(0.99, cpu_rate[30s])
+                 vs the dataset's TRUE pooled p99.
+  x = total cost = equal-weight mean of (edge CPU cores, wire egress KB/s),
+                   each normalized so the raw-forwarding anchor = 1.0.
+
+Topology per arm (fresh backend + producer per arm — delta-query
+queryability requires a clean per-series-base backend):
+
+    otel-app (SDK pre-aggregation: raw-buffer | dd-full | dd-delta,
+              producer-side warm-sample-p)
+        --OTLP/gRPC(:4317, gzip)-->  data_plane backend (sketch store)
+        --PromQL(:9091)-->  query p99  vs  exact offline pooled GT (gt_eval)
+
+Cost is measured per arm:
+  * wire egress  = iptables byte counter on tcp dport 4317 (loopback OTLP),
+                   divided by the replay wall-clock -> KB/s.
+  * edge CPU     = producer process utime+stime delta (clock ticks -> seconds),
+                   divided by replay wall-clock -> cores.
+
+The two knobs the sweep exercises:
+  * sampling p   -> -warm-sample-p (drops raw points before the SDK
+                    aggregation; admitted ingest ~ p, smaller wire, ~constant
+                    pooled accuracy until small-N degrades).
+  * delta/ε_cdm  -> -agg dd-delta (SDK ships delta-transmitted sketch state;
+                    cuts steady-state wire vs dd-full). The trace is LOOPED so
+                    multiple SDK windows elapse and the per-window delta benefit
+                    emerges at steady state (the first-window-full-state is
+                    amortized — see the honest caveats in RESULTS.md).
+
+NB: this minimal topology runs the SDK pre-aggregation as the "edge" (the
+otel-app SDK View IS the edge sketch). A separate asap-otel collector hop is
+NOT inserted: it would only forward the same SDK sketch envelope to the same
+backend, adding a constant per-arm cost that cancels in the raw-normalized
+ratio. We state this explicitly so the cost axis is unambiguous.
+"""
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import signal
+import subprocess
+import sys
+import time
+import urllib.parse
+import urllib.request
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parent
+sys.path.insert(0, str(ROOT))
+import gt_eval  # noqa: E402
+
+REPO = Path("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/mydata/ASAPCollector")
+BACKEND_BIN = Path("/mydata/ASAPQuery-backend/target/release/data_plane")
+OTEL_APP = REPO / "otel-app" / "otel-app"
+METRIC = "google_cluster_2019_cpu_rate"
+# Wide range so the query MERGES all tumbling sub-windows of the single
+# replay pass into one pooled p99 (matches the global pooled GT). The
+# headline query is quantile_over_time(0.99, cpu_rate[30s]); we widen the
+# range only so the multi-window single pass is queryable as one pool.
+QUERY = f"quantile_over_time(0.99, {METRIC}[3600s])"
+GRPC_PORT = 4317
+HTTP_PORT_BASE = 4318
+QUERY_PORT = 9091
+IPT_CHAIN = "PARETOBW"
+CLK_TCK = os.sysconf("SC_CLK_TCK")
+
+
+# ---------------------------------------------------------------------------
+# wire metering via iptables (loopback byte counter on OTLP ingest port)
+# ---------------------------------------------------------------------------
+def ipt(*args: str) -> subprocess.CompletedProcess:
+    return subprocess.run(["sudo", "-n", "iptables", *args],
+                          capture_output=True, text=True)
+
+
+def ipt_setup() -> None:
+    ipt("-N", IPT_CHAIN)
+    ipt("-F", IPT_CHAIN)
+    ipt("-A", IPT_CHAIN, "-p", "tcp", "--dport", str(GRPC_PORT))
+    if ipt("-C", "INPUT", "-j", IPT_CHAIN).returncode != 0:
+        ipt("-I", "INPUT", "-j", IPT_CHAIN)
+    ipt("-Z", IPT_CHAIN)
+
+
+def ipt_teardown() -> None:
+    ipt("-D", "INPUT", "-j", IPT_CHAIN)
+    ipt("-F", IPT_CHAIN)
+    ipt("-X", IPT_CHAIN)
+
+
+def ipt_zero() -> None:
+    ipt("-Z", IPT_CHAIN)
+
+
+def ipt_bytes() -> int:
+    out = ipt("-L", IPT_CHAIN, "-v", "-n", "-x").stdout
+    for line in out.splitlines():
+        if f"dpt:{GRPC_PORT}" in line:
+            return int(line.split()[1])
+    return 0
+
+
+# ---------------------------------------------------------------------------
+# process CPU sampling via /proc/<pid>/stat (utime+stime, fields 14,15)
+# ---------------------------------------------------------------------------
+def proc_cpu_ticks(pid: int) -> int:
+    try:
+        parts = Path(f"/proc/{pid}/stat").read_text().split()
+        # field indices 13,14 (0-based) = utime,stime
+        return int(parts[13]) + int(parts[14])
+    except Exception:
+        return 0
+
+
+# ---------------------------------------------------------------------------
+# backend lifecycle
+# ---------------------------------------------------------------------------
+def port_free(port: int) -> bool:
+    out = subprocess.run(["ss", "-ltn"], capture_output=True, text=True).stdout
+    return f":{port} " not in out and f":{port}\n" not in out and \
+        not any(f":{port}" in ln.split()[3] for ln in out.splitlines()[1:]
+                if len(ln.split()) > 3)
+
+
+def wait_ports_free(ports: list[int], timeout_s: float = 20.0) -> bool:
+    deadline = time.time() + timeout_s
+    while time.time() < deadline:
+        if all(port_free(p) for p in ports):
+            return True
+        time.sleep(0.5)
+    return False
+
+
+def start_backend(streaming_cfg: Path, logdir: Path) -> subprocess.Popen:
+    log = open(logdir / "data_plane.log", "w")
+    env = dict(os.environ, RUST_LOG="info")
+    p = subprocess.Popen(
+        [str(BACKEND_BIN),
+         "--streaming-config", str(streaming_cfg),
+         "--enable-otel-ingest",
+         "--otel-grpc-port", str(GRPC_PORT),
+         "--otel-http-port", str(HTTP_PORT_BASE),
+         "--query-port", str(QUERY_PORT),
+         "--prometheus-scrape-interval", "60",
+         "--output-dir", str(logdir),
+         "--log-level", "warn"],
+        stdout=log, stderr=subprocess.STDOUT, env=env)
+    return p
+
+
+def await_query_ready(timeout_s: float = 30.0) -> bool:
+    url = f"http://127.0.0.1:{QUERY_PORT}/api/v1/query?" + urllib.parse.urlencode(
+        {"query": "vector(1)"})
+    deadline = time.time() + timeout_s
+    while time.time() < deadline:
+        try:
+            with urllib.request.urlopen(url, timeout=3) as r:
+                if r.status == 200:
+                    return True
+        except Exception:
+            time.sleep(0.5)
+    return False
+
+
+def query_p99() -> float | None:
+    url = f"http://127.0.0.1:{QUERY_PORT}/api/v1/query?" + urllib.parse.urlencode(
+        {"query": QUERY})
+    try:
+        with urllib.request.urlopen(url, timeout=15) as r:
+            body = json.loads(r.read().decode())
+    except Exception as e:
+        print(f"  query error: {e}", file=sys.stderr)
+        return None
+    data = body.get("data") or {}
+    res = data.get("result") or []
+    if not res:
+        return None
+    # pooled single-series -> one result; if multiple, take max (global p99
+    # is dominated by the heaviest series in the single-series-pool design,
+    # but the pool design yields exactly one series).
+    vals = []
+    for s in res:
+        v = s.get("value")
+        if v and v[1] is not None:
+            vals.append(float(v[1]))
+    return max(vals) if vals else None
+
+
+def query_latency(n: int = 25) -> tuple[float | None, float | None]:
+    """Time the warm quantile QUERY n times; return (p50_ms, p99_ms)."""
+    url = f"http://127.0.0.1:{QUERY_PORT}/api/v1/query?" + urllib.parse.urlencode(
+        {"query": QUERY})
+    lat: list[float] = []
+    for _ in range(n):
+        t = time.perf_counter()
+        try:
+            with urllib.request.urlopen(url, timeout=15) as r:
+                r.read()
+        except Exception:
+            continue
+        lat.append((time.perf_counter() - t) * 1000.0)
+    if not lat:
+        return None, None
+    lat.sort()
+    return round(lat[len(lat) // 2], 2), round(lat[min(len(lat) - 1, int(len(lat) * 0.99))], 2)
+
+
+def proc_rss_mb(pid: int) -> float:
+    """Resident set size (MB) of a live process."""
+    try:
+        for line in Path(f"/proc/{pid}/status").read_text().splitlines():
+            if line.startswith("VmRSS:"):
+                return round(int(line.split()[1]) / 1024.0, 1)  # kB -> MB
+    except Exception:
+        pass
+    return 0.0
+
+
+def query_freshness_ms() -> float | None:
+    """Data freshness = now - emit_ts of the most-recent queryable warm sample.
+    The freshness probe counter's value IS its emission epoch-ms (see
+    measure_freshness.sh); query the warm tier for it, matching the probe name
+    with or without the OTLP->metric suffix."""
+    for name in ("http_freshness_probe_warm",
+                 "http_freshness_probe_warm_milliseconds_total",
+                 '{__name__=~"http_freshness_probe_warm.*"}'):
+        url = f"http://127.0.0.1:{QUERY_PORT}/api/v1/query?" + urllib.parse.urlencode(
+            {"query": name})
+        try:
+            with urllib.request.urlopen(url, timeout=10) as r:
+                body = json.loads(r.read().decode())
+        except Exception:
+            continue
+        res = (body.get("data") or {}).get("result") or []
+        now_ms = time.time() * 1000.0
+        best = None
+        for s in res:
+            v = s.get("value")
+            if v and v[1] is not None:
+                lag = now_ms - float(v[1])
+                if 0 <= lag < 3_600_000 and (best is None or lag < best):
+                    best = lag
+        if best is not None:
+            return round(best, 1)
+    return None
+
+
+# ---------------------------------------------------------------------------
+# one arm
+# ---------------------------------------------------------------------------
+def run_arm(name: str, agg: str, p: float, scale: float, duration_s: float,
+            loop: bool, sdk_window: str, streaming_cfg: Path,
+            trace_csv: Path, logroot: Path) -> dict:
+    logdir = logroot / name
+    logdir.mkdir(parents=True, exist_ok=True)
+
+    # fresh backend: ensure prior backend's ports are released first
+    if not wait_ports_free([GRPC_PORT, QUERY_PORT, HTTP_PORT_BASE]):
+        raise RuntimeError(f"{name}: ports still bound before backend start")
+    bk = start_backend(streaming_cfg, logdir)
+    if not await_query_ready() or bk.poll() is not None:
+        try:
+            bk.send_signal(signal.SIGTERM)
+        except Exception:
+            pass
+        raise RuntimeError(f"{name}: backend not ready (pid alive="
+                           f"{bk.poll() is None})")
+
+    ipt_zero()
+    plog = open(logdir / "producer.log", "w")
+    cmd = [str(OTEL_APP),
+           "-target", f"127.0.0.1:{GRPC_PORT}",
+           "-trace-file", str(trace_csv),
+           "-trace-metric-name", METRIC,
+           "-trace-scale", str(scale),
+           f"-trace-loop={'true' if loop else 'false'}",
+           "-agg", agg,
+           "-sdk-window", sdk_window,
+           "-warm-sample-p", str(p),
+           "-five-sketch=false", "-freshness-probes=true",
+           "-seed", "12345"]
+    if duration_s > 0:
+        cmd += ["-duration", f"{int(duration_s)}s"]
+    prod = subprocess.Popen(cmd, stdout=plog, stderr=subprocess.STDOUT)
+
+    t0 = time.time()
+    cpu0 = proc_cpu_ticks(prod.pid)
+    # poll cpu (+ peak RSS for backend & producer) while running
+    last_cpu = cpu0
+    bk_rss = prod_rss = 0.0
+    while prod.poll() is None:
+        c = proc_cpu_ticks(prod.pid)
+        if c:
+            last_cpu = c
+        bk_rss = max(bk_rss, proc_rss_mb(bk.pid))
+        prod_rss = max(prod_rss, proc_rss_mb(prod.pid))
+        time.sleep(0.25)
+        # generous guard: single-pass at the sweep scale is ~20-25s; cap at
+        # max(duration, 120) + 30 so a slow pass is never truncated early.
+        if time.time() - t0 > max(duration_s, 120) + 30:
+            prod.send_signal(signal.SIGTERM)
+            break
+    t1 = time.time()
+    wall = t1 - t0
+    cpu_ticks = max(last_cpu - cpu0, 0)
+    cpu_cores = (cpu_ticks / CLK_TCK) / wall if wall > 0 else 0.0
+
+    wire_bytes = ipt_bytes()
+    wire_kbps = (wire_bytes / 1024.0) / wall if wall > 0 else 0.0
+
+    # parse REPLAY_STATS (last line) for admitted/candidate
+    admitted = candidate = 0
+    for line in (logdir / "producer.log").read_text().splitlines():
+        if "REPLAY_STATS" in line:
+            for tok in line.split():
+                if tok.startswith("candidate="):
+                    candidate = int(tok.split("=")[1])
+                elif tok.startswith("admitted="):
+                    admitted = int(tok.split("=")[1])
+    adm_per_s = admitted / wall if wall > 0 else 0.0
+
+    # FRESHNESS = sample-generation → backend-queryable. Poll from producer end
+    # (t1, ≈ when the last samples were generated) until the metric first answers;
+    # that lag is the staleness floor (window seal + scrape lookback).
+    poll0 = time.time()
+    p99 = None
+    while time.time() - poll0 < 90:
+        p99 = query_p99()
+        if p99 is not None:
+            break
+        time.sleep(1.0)
+    freshness_ms = round((time.time() - t1) * 1000.0) if p99 is not None else None
+    # backend still alive: query latency + backend RSS
+    lat_p50, lat_p99 = query_latency()
+    bk_rss = max(bk_rss, proc_rss_mb(bk.pid))
+
+    bk.send_signal(signal.SIGTERM)
+    try:
+        bk.wait(timeout=10)
+    except subprocess.TimeoutExpired:
+        bk.kill()
+    time.sleep(1.0)
+
+    return {
+        "name": name, "agg": agg, "p": p, "scale": scale, "loop": loop,
+        "wall_s": round(wall, 2),
+        "wire_bytes": wire_bytes, "wire_kbps": round(wire_kbps, 3),
+        "cpu_ticks": cpu_ticks, "cpu_cores": round(cpu_cores, 4),
+        "candidate": candidate, "admitted": admitted,
+        "admitted_per_s": round(adm_per_s, 1),
+        "p99": p99,
+        # NEW integrated metrics:
+        "lat_p50_ms": lat_p50, "lat_p99_ms": lat_p99,
+        "freshness_ms": freshness_ms,
+        "backend_rss_mb": round(bk_rss, 1), "producer_rss_mb": round(prod_rss, 1),
+    }
+
+
+def main() -> int:
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--trace-csv", type=Path, default=Path("/tmp/gct-cpu-pooled.csv"))
+    ap.add_argument("--jsonl", type=Path, default=Path("/tmp/gct-otlp.jsonl"))
+    ap.add_argument("--streaming-config", type=Path,
+                    default=Path("/tmp/pareto/streaming-config.yaml"))
+    ap.add_argument("--out-dir", type=Path, default=Path("/tmp/pareto/results"))
+    ap.add_argument("--duration", type=float, default=0.0,
+                    help="per-arm producer duration cap; 0 = single full pass")
+    args = ap.parse_args()
+
+    args.out_dir.mkdir(parents=True, exist_ok=True)
+    logroot = args.out_dir / "logs"
+    logroot.mkdir(parents=True, exist_ok=True)
+
+    # exact pooled ground truth p99 over the whole trace
+    rows = gt_eval.load_rows(args.jsonl)
+    gt_p99 = gt_eval.gt_quantile(rows, {"metric": METRIC, "q": 0.99, "by": []})
+    print(f"GT pooled p99 = {gt_p99:.6f}  ({len(rows)} jsonl rows)", file=sys.stderr)
+
+    # SINGLE-PASS grid (loop=False): each arm replays the 100k-point trace
+    # exactly once so sampling p genuinely thins the realized sample set
+    # (looping would re-cover the full value distribution and hide ε_s).
+    # SCALE compresses the ~31d trace timestamps into ~20s wall; SDK_WIN=5s
+    # yields ~4 tumbling sub-windows the wide-range query merges into one
+    # pooled p99 (matching the global pooled GT).
+    SCALE = 134_000
+    SDK_WIN = "5s"
+    # Integrated ε-sweep: rows are the admission p (== the ε-floor p=1/(1+ε²·rate)
+    # the autonomous coordinator would set for the corresponding ε). raw anchor +
+    # DDSketch warm at a p-grid spanning the ε range. (dd-delta dropped from the
+    # first integrated table to keep the run short; re-add for the ε_cdm axis.)
+    SDK_WIN = "5s"
+    arms = [
+        dict(name="raw",          agg="raw-buffer", p=1.0, anchor=True),
+        dict(name="dd_p1.00",     agg="dd-full",    p=1.0),
+        dict(name="dd_p0.50",     agg="dd-full",    p=0.5),
+        dict(name="dd_p0.25",     agg="dd-full",    p=0.25),
+        dict(name="dd_p0.10",     agg="dd-full",    p=0.1),
+        dict(name="dd_p0.05",     agg="dd-full",    p=0.05),
+    ]
+
+    ipt_setup()
+    results = []
+    try:
+        for a in arms:
+            anchor = a.get("anchor", False)
+            print(f"\n=== arm {a['name']} (agg={a['agg']} p={a['p']}"
+                  f"{' ANCHOR' if anchor else ''}) ===", file=sys.stderr)
+            r = run_arm(a["name"], a["agg"], a["p"], SCALE, args.duration,
+                        loop=False, sdk_window=SDK_WIN,
+                        streaming_cfg=args.streaming_config,
+                        trace_csv=args.trace_csv, logroot=logroot)
+            r["anchor"] = anchor
+            if anchor:
+                # raw forwarding is exact by definition
+                r["rel_err"] = 0.0
+                r["accuracy"] = 1.0
+                r["p99_measured"] = r["p99"]
+            elif r["p99"] is not None and gt_p99 > 0:
+                r["rel_err"] = abs(r["p99"] - gt_p99) / gt_p99
+                r["accuracy"] = 1.0 - r["rel_err"]
+            else:
+                r["rel_err"] = None
+                r["accuracy"] = None
+            # implied ε for this admission p: the ε whose floor 1/(1+ε²·rate)=p,
+            # using this run's per-window rate (SDK_WIN=5s). p=1 ⇒ ε→0.
+            rate_w = r["candidate"] / max(1.0, r["wall_s"] / 5.0)
+            r["rate_window"] = round(rate_w)
+            r["epsilon"] = 0.0 if r["p"] >= 1.0 else round(((1.0 / r["p"] - 1.0) / rate_w) ** 0.5, 4)
+            print(f"  -> acc={r['accuracy']} lat_p99={r.get('lat_p99_ms')}ms "
+                  f"fresh={r.get('freshness_ms')}ms bkRSS={r.get('backend_rss_mb')}MB "
+                  f"cpu={r['cpu_cores']:.3f} wire={r['wire_kbps']:.1f}kbps ε≈{r['epsilon']}",
+                  file=sys.stderr)
+            results.append(r)
+    finally:
+        subprocess.run(["pkill", "-f", "data_plane --streaming-config"],
+                       capture_output=True)
+        ipt_teardown()
+
+    # normalize to raw anchor
+    raw = next(r for r in results if r["name"] == "raw")
+    raw_cpu = raw["cpu_cores"] or 1e-9
+    raw_wire = raw["wire_kbps"] or 1e-9
+    for r in results:
+        r["cpu_norm"] = round(r["cpu_cores"] / raw_cpu, 4)
+        r["wire_norm"] = round(r["wire_kbps"] / raw_wire, 4)
+        r["cost_total"] = round(0.5 * (r["cpu_norm"] + r["wire_norm"]), 4)
+
+    payload = {"gt_p99": gt_p99, "raw_anchor": raw["name"],
+               "cost_def": ("total cost = 0.5*(cpu_norm + wire_norm), each "
+                            "normalized to raw=1.0; cpu = producer/edge "
+                            "utime+stime cores, wire = OTLP gzipped bytes on "
+                            "tcp dport 4317 (iptables) / wall. Backend CPU is "
+                            "OUT of total (same backend binary per arm). Both "
+                            "sub-axes reported so either can be dropped."),
+               "query": QUERY,
+               "results": results}
+    (args.out_dir / "sweep-results.json").write_text(
+        json.dumps(payload, indent=2) + "\n")
+
+    # ----- the integrated table (the deliverable) -----
+    hdr = (f"\n{'arm':9s} {'ε':>6s} {'p':>5s} | {'accuracy':>8s} | "
+           f"{'lat_p50':>7s} {'lat_p99':>7s} | {'fresh_ms':>8s} | "
+           f"{'bk_RSS':>6s} {'pr_RSS':>6s} | {'cpu':>6s} {'wire':>7s}")
+    print(f"\n=== INTEGRATED ε-SWEEP (GT p99={gt_p99:.4f}) ===")
+    print("  cols: accuracy=1−rel_err vs GT · lat ms · freshness gen→queryable ms ·"
+          " RSS MB · cpu cores · wire kbps")
+    print(hdr)
+    for r in results:
+        acc = r.get("accuracy")
+        print(f"{r['name']:9s} {r.get('epsilon',0):>6} {r['p']:>5} | "
+              f"{('%.4f'%acc) if acc is not None else 'NA':>8s} | "
+              f"{str(r.get('lat_p50_ms')):>7s} {str(r.get('lat_p99_ms')):>7s} | "
+              f"{str(r.get('freshness_ms')):>8s} | "
+              f"{r.get('backend_rss_mb',0):>6} {r.get('producer_rss_mb',0):>6} | "
+              f"{r['cpu_cores']:>6.3f} {r['wire_kbps']:>7.1f}")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/datasets_eval/google_cluster/run.py b/datasets_eval/google_cluster/run.py
index 63ee6dd13..227cd58eb 100644
--- a/datasets_eval/google_cluster/run.py
+++ b/datasets_eval/google_cluster/run.py
@@ -148,6 +148,7 @@ def _replay_otlp_grpc(
     pace_factor: float,
     max_lines: int,
     wall_clock_anchor: bool = False,
+    anchor_span_s: float = 0.0,
 ) -> int:
     """OTLP/gRPC sender. Lazy-imports opentelemetry-proto deps.
 
@@ -203,6 +204,28 @@ def _replay_otlp_grpc(
     # late rows). The per-family arms (~200k rows) replay in << 60s; for a
     # multi-minute all-families replay, slice per family or widen the window.
     anchor_now_ns = time.time_ns()
+    # anchor_span_s > 0: instead of collapsing EVERY point onto a single
+    # instant, spread the points across the last `anchor_span_s` seconds with a
+    # distinct nanosecond each (still one window when span < window_duration and
+    # the send is boundary-aligned). Single-instant anchoring gives every event
+    # of a key an IDENTICAL (series, ts, value) tuple, so count-type sketches
+    # (CountSketch/CountMin: value==1.0 per event) see the duplicates collapse
+    # and lose all per-key multiplicity — topk/frequency then read ~1 per key.
+    # A quantile workload is unaffected (its values differ), but a spread is
+    # strictly safer for it too. Requires a pre-count to size the step.
+    span_ns = int(anchor_span_s * 1_000_000_000)
+    total_pts = 0
+    if wall_clock_anchor and span_ns > 0:
+        with open(jsonl_path, "r", encoding="utf-8") as _fp:
+            total_pts = sum(1 for _l in _fp if _l.strip())
+    step_ns = span_ns // max(1, total_pts - 1) if total_pts > 1 else 0
+    # Spread FORWARD from now: [now, now+span]. now is boundary-aligned to a
+    # fresh window start, so the whole span lands in the OPEN window. A backward
+    # spread [now-span, now] would backdate points into the PREVIOUS window,
+    # which has already sealed on wall-clock — the agent then drops them as
+    # late-arriving (only the points nearest `now` survive). Keep span <
+    # window_duration so it doesn't spill into the next window.
+    anchor_start_ns = anchor_now_ns
 
     def flush(rows: list[dict[str, Any]]) -> None:
         if not rows:
@@ -224,7 +247,7 @@ def flush(rows: list[dict[str, Any]]) -> None:
                 dp = metric.gauge.data_points.add()
                 dp.as_double = float(r["value"])
                 if wall_clock_anchor:
-                    dp.time_unix_nano = anchor_now_ns
+                    dp.time_unix_nano = r.get("_ts_ns", anchor_now_ns)
                 else:
                     dp.time_unix_nano = int(r["timestamp_ms"]) * 1_000_000
                 for k, v in sorted(r["attributes"].items()):
@@ -261,6 +284,8 @@ def flush(rows: list[dict[str, Any]]) -> None:
                     sleep_ns = target_ns - time.time_ns()
                     if sleep_ns > 0:
                         time.sleep(sleep_ns / 1e9)
+                if wall_clock_anchor and step_ns > 0:
+                    obj["_ts_ns"] = anchor_start_ns + n * step_ns
                 batch.append(obj)
                 if len(batch) >= BATCH_SIZE:
                     flush(batch)
@@ -281,7 +306,8 @@ def cmd_replay(args: argparse.Namespace) -> int:
     if args.dry_run or not args.endpoint:
         return _replay_dry_run(args.jsonl, args.max_lines)
     rc = _replay_otlp_grpc(args.jsonl, args.endpoint, args.pace_factor, args.max_lines,
-                           wall_clock_anchor=getattr(args, "wall_clock_anchor", False))
+                           wall_clock_anchor=getattr(args, "wall_clock_anchor", False),
+                           anchor_span_s=getattr(args, "anchor_span_s", 0.0))
     if rc == 4:
         return _replay_dry_run(args.jsonl, args.max_lines)
     return rc
@@ -458,6 +484,12 @@ def main(argv: list[str] | None = None) -> int:
                          "Required for recent-range PromQL ([Ns]) to intersect "
                          "the warm sketch windows. Timestamp-only; GT unchanged. "
                          "Replay must finish within one window_duration.")
+    pr.add_argument("--anchor-span-s", type=float, default=0.0,
+                    help="With --wall-clock-anchor, spread points across the last "
+                         "N seconds (distinct ns each) instead of one instant, so "
+                         "count-type sketches (value==1.0 per event) keep per-key "
+                         "multiplicity. Keep N < window_duration so it stays one "
+                         "window (e.g. 50 for a 60s window).")
     pr.add_argument("--dry-run", action="store_true",
                     help="Force dry-run even with --endpoint set.")
     pr.set_defaults(func=cmd_replay)
diff --git a/datasets_eval/latency/.gitignore b/datasets_eval/latency/.gitignore
index 5630d7f07..b270d4224 100644
--- a/datasets_eval/latency/.gitignore
+++ b/datasets_eval/latency/.gitignore
@@ -1 +1,2 @@
 replay-warm.jsonl
+replay-cold.jsonl
diff --git a/datasets_eval/latency/agent-cold-ship.yaml b/datasets_eval/latency/agent-cold-ship.yaml
new file mode 100644
index 000000000..9c61f00ee
--- /dev/null
+++ b/datasets_eval/latency/agent-cold-ship.yaml
@@ -0,0 +1,61 @@
+# Cold-ON edge agent for the Fig 7 cold-fallback latency arm.
+#
+# Derived from datasets_eval/multisketch/agent-ddsketch-coldon.yaml, but
+# with a COMPLETE cold block (ship_endpoint + block_duration +
+# external_labels) so the edge actually SHIPS cold ASAPFRG1 fragments to
+# the gorilla-merger. The multisketch coldon config only set
+# `cold: {enabled: true}` with NO ship_endpoint, which the asapedge
+# processor treats as DRAIN-ONLY (no shipping) — that is why the
+# original cold arm landed an empty MinIO (config.go: "Empty =>
+# drain-only (no shipping)").
+#
+# The lossless raw `google_cluster_2019_cpu_rate` Sum family (tier: both)
+# is the series that ships cold; the data-plane routes its queries to the
+# gorilla cold tier (backend-storage-routing-coldon.yaml).
+receivers:
+  otlp:
+    protocols:
+      grpc: {endpoint: 0.0.0.0:4317, max_recv_msg_size_mib: 4096}
+      http: {endpoint: 0.0.0.0:4318}
+processors:
+  batch:
+    send_batch_size: 800
+    send_batch_max_size: 1500
+  memory_limiter: {check_interval: 1s, limit_mib: 8192, spike_limit_mib: 1024}
+  asap_edge:
+    shard_count: 12
+    window_duration: 60s
+    drop_original: true
+    max_series: 200000
+    delta_transmission: false
+    metrics:
+      - {metric: google_cluster_2019_cpu_rate, family: sum, tier: both}
+      - {metric: google_cluster_2019_memory_usage, family: sum, aggregate_by: [zone], tier: both}
+      - metric: google_cluster_2019_cpu_rate_q_ddsketch
+        family: "ddsketch"
+        tier: "both"
+        delta_transmission: true
+    cold:
+      enabled: true
+      ship_endpoint: http://gorilla-merger:10908/ingest/gorilla
+      block_duration: 60s
+      reorder_grace: 2s
+      external_labels:
+        # MUST be non-empty: Thanos rejects a block with empty external
+        # labels ("empty external labels are not allowed for Thanos
+        # block"); matches the merger's --external-labels cluster=asap-mvp.
+        cluster: asap-mvp
+    control_channel: {enabled: false}
+exporters:
+  otlp/backend:
+    endpoint: data-plane:14317
+    tls: {insecure: true}
+    timeout: 120s
+    sending_queue: {enabled: true, num_consumers: 4, queue_size: 5000}
+service:
+  pipelines:
+    metrics: {receivers: [otlp], processors: [memory_limiter, asap_edge, batch], exporters: [otlp/backend]}
+  telemetry:
+    metrics:
+      level: detailed
+      readers: [{pull: {exporter: {prometheus: {host: 0.0.0.0, port: 8890}}}}]
diff --git a/datasets_eval/latency/backend-storage-routing-coldon.yaml b/datasets_eval/latency/backend-storage-routing-coldon.yaml
new file mode 100644
index 000000000..96387bf3c
--- /dev/null
+++ b/datasets_eval/latency/backend-storage-routing-coldon.yaml
@@ -0,0 +1,29 @@
+# Cold-arm storage-routing table (Fig 7 cold-fallback latency arm).
+#
+# Routes the lossless raw `google_cluster_2019_cpu_rate` series — the
+# Sum family the cold-enabled agent ships to the gorilla cold tier
+# (MinIO via the merger) — to `gorilla_object_store`, so its PromQL
+# queries are answered by the ThanosQueryEngine (data_source =
+# thanos_query). This is what makes the COLD/archive path observable
+# end-to-end: an instant query against this metric is dispatched
+# through EngineRouter to thanos-query, which resolves the answer over
+# the gorilla-merger StoreAPI + thanos-store-gateway (S3/MinIO blocks).
+#
+# `double_write` routes the metric to BOTH warm sketches and the
+# archive, with the cost-aware dispatcher picking per query. We use
+# the single-target `gorilla_object_store` here to GUARANTEE every
+# timed query lands on the cold path (no warm shortcut) — we are
+# measuring the cold arm, so we force the cold engine.
+#
+# Used by datasets_eval/latency/stack-coldon.sh (cold-ON stack).
+
+default: sketch_store
+
+routes:
+  - metric: google_cluster_2019_cpu_rate
+    targets:
+      - backend: gorilla_object_store
+        # all shapes route to the cold archive — sum / count /
+        # quantile_over_time over the raw shipped samples are all
+        # computed by thanos-query over the merger StoreAPI.
+        applies_to_query_shape: [count, topk, rate_post_hoc, quantile, sum, last_over_time, other]
diff --git a/datasets_eval/latency/cold_latency_replay.py b/datasets_eval/latency/cold_latency_replay.py
new file mode 100644
index 000000000..1e6a317ff
--- /dev/null
+++ b/datasets_eval/latency/cold_latency_replay.py
@@ -0,0 +1,128 @@
+#!/usr/bin/env python3
+"""Cold-arm query-latency replay (Fig 7 cold-fallback arm).
+
+Mirrors deploy/mvp-singlenode/scripts/metricsql_replay.py (warm arm) but
+pins the PromQL *evaluation timestamp* (`time=<at_time>`) to the instant
+the cold workload was anchored at, so every instant query deterministically
+intersects the cold-archived window in MinIO/Thanos. (The warm arm queried
+at live `now` because its in-memory warm window sat at `now`; the cold
+window sits at a fixed past instant — the ship takes ~one window to land —
+so we pin the eval time to it. The pin changes only WHICH timestamp the
+backend evaluates at; the per-query server-side latency it measures is
+identical in kind to the warm arm.)
+
+Fires the query mix at a fixed QPS against the data-plane query surface
+(:9091/api/v1/query), captures per-query wall-clock latency + the served
+`data_source` (asap_query=warm vs thanos_query=cold) + result vector, and
+writes a JSONL identical in schema to the warm replay so
+compute_latency.py reduces both arms the same way.
+
+Usage:
+  cold_latency_replay.py --target http://127.0.0.1:9091 \
+    --queries queries-latency-cold.json --at-time <unix_seconds> \
+    --qps 15 --duration 40 --out replay-cold.jsonl
+"""
+from __future__ import annotations
+import argparse, datetime as dt, json, sys, threading, time
+import urllib.parse, urllib.request, urllib.error
+
+
+def run_query(target: str, metricsql: str, at_time: float | None,
+              timeout_s: float = 10.0):
+    params = {"query": metricsql}
+    if at_time is not None:
+        params["time"] = f"{at_time:.3f}"
+    url = f"{target.rstrip('/')}/api/v1/query?" + urllib.parse.urlencode(params)
+    started = time.perf_counter()
+    try:
+        with urllib.request.urlopen(url, timeout=timeout_s) as resp:
+            code = resp.getcode()
+            body = resp.read().decode("utf-8", errors="replace")
+    except urllib.error.HTTPError as e:
+        return (time.perf_counter() - started) * 1000.0, {
+            "status": "http_error", "http_code": e.code, "result": None,
+            "result_type": None, "data_source": None, "error": str(e)}
+    except Exception as e:
+        return (time.perf_counter() - started) * 1000.0, {
+            "status": "timeout", "http_code": None, "result": None,
+            "result_type": None, "data_source": None, "error": str(e)}
+    duration_ms = (time.perf_counter() - started) * 1000.0
+    try:
+        parsed = json.loads(body)
+    except json.JSONDecodeError as e:
+        return duration_ms, {"status": "json_error", "http_code": code,
+                             "result": None, "result_type": None,
+                             "data_source": None, "error": str(e)}
+    data = parsed.get("data") or {}
+    data_source = None
+    for info in (parsed.get("infos") or []):
+        if isinstance(info, str) and info.startswith("data_source:"):
+            data_source = info.split(":", 1)[1].strip()
+    return duration_ms, {
+        "status": parsed.get("status", "unknown"), "http_code": code,
+        "result": data.get("result"), "result_type": data.get("resultType"),
+        "data_source": data_source}
+
+
+def main() -> int:
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--target", default="http://127.0.0.1:9091")
+    ap.add_argument("--queries", required=True)
+    ap.add_argument("--at-time", type=float, default=None,
+                    help="Unix seconds to pin the PromQL eval timestamp to. "
+                         "Omit to query live now (warm-style).")
+    ap.add_argument("--qps", type=float, default=15.0)
+    ap.add_argument("--duration", type=float, default=40.0)
+    ap.add_argument("--out", required=True)
+    args = ap.parse_args()
+
+    with open(args.queries) as f:
+        queries = json.load(f)
+    if not queries:
+        sys.exit("no queries")
+
+    interval = 1.0 / args.qps
+    deadline = time.time() + args.duration
+    lock = threading.Lock()
+    rows: list[dict] = []
+    qi = 0
+    n_sent = 0
+    t0 = time.time()
+    while time.time() < deadline:
+        q = queries[qi % len(queries)]
+        qi += 1
+        dur_ms, res = run_query(args.target, q["metricsql"], args.at_time)
+        rec = {
+            "ts": dt.datetime.now(dt.timezone.utc).isoformat(),
+            "query": q["metricsql"], "kind": q.get("kind", "other"),
+            "duration_ms": round(dur_ms, 4), "status": res["status"],
+            "http_code": res.get("http_code"),
+            "result_type": res.get("result_type"),
+            "result": res.get("result"),
+            "data_source": res.get("data_source"),
+            "n_result_series": len(res.get("result") or []),
+        }
+        rows.append(rec)
+        n_sent += 1
+        # fixed-rate pacing
+        next_at = t0 + n_sent * interval
+        sleep = next_at - time.time()
+        if sleep > 0:
+            time.sleep(sleep)
+
+    with open(args.out, "w") as f:
+        for r in rows:
+            f.write(json.dumps(r) + "\n")
+    # quick summary
+    from collections import Counter
+    ds = Counter(r["data_source"] for r in rows)
+    st = Counter(r["status"] for r in rows)
+    empt = sum(1 for r in rows if not r["result"])
+    print(f"replay: {len(rows)} queries -> {args.out}")
+    print(f"  data_source: {dict(ds)}")
+    print(f"  status: {dict(st)}  empty_results: {empt}")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/datasets_eval/latency/compute_latency.py b/datasets_eval/latency/compute_latency.py
index f25fd6b71..5ac4c1497 100644
--- a/datasets_eval/latency/compute_latency.py
+++ b/datasets_eval/latency/compute_latency.py
@@ -41,21 +41,36 @@ def pct(sorted_vals, p):
     return sorted_vals[lo] + (sorted_vals[hi] - sorted_vals[lo]) * frac
 
 
-def summarize(rows, label, require_warm=True):
+def _n_result(r) -> int:
+    """Result count from either the full replay schema (`result` vector) or
+    the slim per-query schema (`n_result_series`)."""
+    res = r.get("result")
+    if res is not None:
+        return len(res)
+    return int(r.get("n_result_series") or 0)
+
+
+def summarize(rows, label, require_real=True, require_source=None):
     """Returns (summary_dict, {kind: [latencies]}, [all_latencies]).
-    Hard-fails if require_warm and any timed query was empty/errored."""
+    Hard-fails if require_real and any timed query was empty/errored, or if
+    require_source is set and any timed query was served by a different
+    data_source (guards that the arm's answers came from the intended tier)."""
     bad = []
+    wrong_src = []
     by_kind: dict[str, list[float]] = {}
     alllat: list[float] = []
     src_counter: dict[str, int] = {}
     for r in rows:
-        res = r.get("result") or []
+        n = _n_result(r)
         src = r.get("data_source")
         src_counter[src] = src_counter.get(src, 0) + 1
-        ok = r.get("status") == "success" and bool(res)
-        if require_warm and not ok:
+        ok = r.get("status") == "success" and n > 0
+        if require_real and not ok:
             bad.append({"query": r.get("query"), "status": r.get("status"),
-                        "n_result": len(res), "data_source": src})
+                        "n_result": n, "data_source": src})
+            continue
+        if require_source is not None and src != require_source:
+            wrong_src.append({"query": r.get("query"), "data_source": src})
             continue
         if not ok:
             continue
@@ -63,10 +78,14 @@ def summarize(rows, label, require_warm=True):
         alllat.append(lat)
         by_kind.setdefault(r["kind"], []).append(lat)
 
-    if require_warm and bad:
+    if require_real and bad:
         sys.exit(f"[{label}] {len(bad)} timed queries returned no real result "
                  f"(empty/errored) — latency would be meaningless. First few: "
                  f"{bad[:3]}")
+    if require_source is not None and wrong_src:
+        sys.exit(f"[{label}] {len(wrong_src)} timed queries were NOT served by "
+                 f"data_source={require_source} (wrong tier — this arm must "
+                 f"measure that tier only). First few: {wrong_src[:3]}")
 
     def stats(vals):
         s = sorted(vals)
@@ -98,24 +117,41 @@ def cdf_xy(vals):
     return s, ys
 
 
+def load_any(path: str):
+    """Load either a JSONL replay log (one object per line) or a JSON array
+    (the committed slim per_query_latency*.json)."""
+    text = Path(path).read_text().lstrip()
+    if text.startswith("["):
+        return json.loads(text)
+    return load(path)
+
+
 def main():
     ap = argparse.ArgumentParser()
-    ap.add_argument("--warm", required=True)
-    ap.add_argument("--cold", default=None)
+    ap.add_argument("--warm", required=True,
+                    help="warm replay JSONL or committed per_query_latency.json")
+    ap.add_argument("--cold", default=None,
+                    help="cold replay JSONL or per_query_latency_cold.json")
     ap.add_argument("--out-json", required=True)
     ap.add_argument("--out-png", required=True)
     args = ap.parse_args()
 
-    warm_rows = load(args.warm)
-    warm_sum, warm_by_kind, warm_all = summarize(warm_rows, "warm", require_warm=True)
+    warm_rows = load_any(args.warm)
+    # Warm arm: every timed query must be a real warm answer (data_source=asap_query).
+    warm_sum, warm_by_kind, warm_all = summarize(
+        warm_rows, "warm", require_real=True, require_source="asap_query")
 
     out = {"warm": warm_sum}
 
     cold_all = None
+    cold_by_kind = None
     if args.cold and Path(args.cold).exists():
-        cold_rows = load(args.cold)
-        # cold arm: don't hard-fail on empties (route may differ); report what landed
-        cold_sum, _, cold_all = summarize(cold_rows, "cold", require_warm=False)
+        cold_rows = load_any(args.cold)
+        # Cold arm: GUARD that every timed query landed real AND was served by
+        # the cold/archive engine (data_source=thanos_query) — we are measuring
+        # the cold-fallback tier, so a warm shortcut would invalidate the arm.
+        cold_sum, cold_by_kind, cold_all = summarize(
+            cold_rows, "cold", require_real=True, require_source="thanos_query")
         out["cold"] = cold_sum
 
     Path(args.out_json).write_text(json.dumps(out, indent=2) + "\n")
@@ -136,28 +172,47 @@ def main():
                 color=palette.get(kind, "#7f7f7f"), lw=1.4, ls="--", alpha=0.85)
     if cold_all:
         x, y = cdf_xy(cold_all)
-        ax.plot(x, y, label=f"cold-fallback ({len(cold_all)} q)",
-                color="#d62728", lw=2.0)
-
+        ax.plot(x, y, label=f"cold-fallback — all ({len(cold_all)} q)",
+                color="#d62728", lw=2.2)
+        cold_palette = {"quantile": "#8c564b", "sum": "#e377c2"}
+        for kind, vals in sorted((cold_by_kind or {}).items()):
+            x, y = cdf_xy(vals)
+            ax.plot(x, y, label=f"cold — {kind} ({len(vals)} q)",
+                    color=cold_palette.get(kind, "#d62728"), lw=1.4, ls="--", alpha=0.85)
+
+    # percentile guide lines: warm (solid grey) + cold (red) overall p50/p99
     for p, ls in ((50, ":"), (99, "-.")):
         v = pct(sorted(warm_all), p)
         ax.axvline(v, color="#888", ls=ls, lw=0.9)
-        ax.text(v, 0.04, f"p{p}={v:.1f}ms", rotation=90, fontsize=7,
+        ax.text(v, 0.04, f"warm p{p}={v:.1f}ms", rotation=90, fontsize=6,
                 va="bottom", ha="right", color="#555")
+        if cold_all:
+            cv = pct(sorted(cold_all), p)
+            ax.axvline(cv, color="#d62728", ls=ls, lw=0.8, alpha=0.6)
+            ax.text(cv, 0.04, f"cold p{p}={cv:.1f}ms", rotation=90, fontsize=6,
+                    va="bottom", ha="right", color="#d62728")
 
     ax.set_xlabel("backend query latency (ms)")
     ax.set_ylabel("CDF (fraction of queries ≤ x)")
-    ax.set_title("Fig 7 — backend query latency CDF (warm sketch tier, single-node loopback)")
+    title = "Fig 7 — backend query latency CDF (single-node loopback)"
+    if cold_all:
+        title = ("Fig 7 — backend query latency CDF: warm sketch tier vs "
+                 "cold-fallback archive\n(single-node loopback)")
+    ax.set_title(title)
     ax.set_ylim(0, 1.02)
     ax.set_xlim(left=0)
     ax.grid(True, alpha=0.3)
-    ax.legend(loc="lower right", fontsize=8)
+    ax.legend(loc="lower right", fontsize=7)
     fig.tight_layout()
     fig.savefig(args.out_png, dpi=140)
     print(f"wrote {args.out_json} and {args.out_png}")
-    print(json.dumps(out["warm"]["overall"], indent=2))
+    print("WARM:", json.dumps(out["warm"]["overall"]))
     for k, v in out["warm"]["by_kind"].items():
-        print(f"  {k:14s} p50={v['p50_ms']:.2f}  p95={v['p95_ms']:.2f}  p99={v['p99_ms']:.2f}  (n={v['n']})")
+        print(f"  warm {k:10s} p50={v['p50_ms']:.2f}  p95={v['p95_ms']:.2f}  p99={v['p99_ms']:.2f}  (n={v['n']})")
+    if cold_all:
+        print("COLD:", json.dumps(out["cold"]["overall"]))
+        for k, v in out["cold"]["by_kind"].items():
+            print(f"  cold {k:10s} p50={v['p50_ms']:.2f}  p95={v['p95_ms']:.2f}  p99={v['p99_ms']:.2f}  (n={v['n']})")
 
 
 if __name__ == "__main__":
diff --git a/datasets_eval/latency/latency_RESULTS.md b/datasets_eval/latency/latency_RESULTS.md
index 9a13fb3ca..8a7006335 100644
--- a/datasets_eval/latency/latency_RESULTS.md
+++ b/datasets_eval/latency/latency_RESULTS.md
@@ -6,8 +6,11 @@
 599-query mix at 15 QPS, overall **p50 = 18.3 ms, p99 = 20.0 ms** on the warm
 SketchStore. The exact lossless `sum` is **p50 1.6 ms / p99 2.3 ms**; the
 691-series DDSketch `quantile_over_time` read is **p50 18.3 ms / p99 20.6 ms**.
-**Cold-fallback arm: NOT measured — blocked by archive/ship fragility (see
-below). Warm-only is reported honestly.**
+**Cold-fallback arm: NOW MEASURED** (cold-ON stack, end-to-end through the
+gorilla cold tier): across a 600-query mix @15 QPS with **600/600
+`data_source=thanos_query`**, the cold/archive-answered PromQL is **p50 22.3 ms
+/ p95 47.2 ms / p99 67.1 ms** — overall **p50 ≈ 1.2× warm, p99 ≈ 3.3× warm**.
+Both arms are reported below; the warm numbers are unchanged.
 
 ---
 
@@ -92,50 +95,119 @@ kind) — no long latency tail on the warm path.
 
 ---
 
-## Cold-fallback arm — attempted, BLOCKED (warm-only reported)
-
-I brought up the **full cold stack** (`stack.sh`: MinIO + Thanos
-store/query/compact + gorilla-merger + data-plane with `ASAP_THANOS_QUERY_URL`
-and `ASAP_GORILLA_S3_*`), with a **cold-enabled** agent
-(`agent-ddsketch-coldon.yaml`: `cold: {enabled: true}`, sketch `tier: both`),
-re-replayed the same trace, and waited for the warm window to seal.
-
-**Result: no data ever reached the cold tier**, so there was nothing to query
-on a cold path:
-
-- data-plane registered `ThanosQueryEngine` (archive `data_source_id=thanos_query`) OK;
-- the gorilla-merger ingest frontend (`:10908`) logged **zero** ingest /
-  append / received-samples lines; its shipper found nothing to ship;
-- **MinIO `asap/` held 0 objects** after the run (both `asap-gorilla` and
-  `asap-gorilla-tsdb` empty);
-- the bare static-config agent emitted **no** cold/ship/gorilla/s3 log lines —
-  the `cold: {enabled: true}` block does not drive a gorilla ship in this
-  static fused-agent path (cold endpoint wiring rides the control channel,
-  which is `{enabled: false}` here);
-- even querying **old timestamps** (1 h ago) still returned `data_source=asap_query`
-  (warm), confirming there was no archived data to fall through to.
-
-This is the archive/ship/routing fragility the task anticipated. Rather than
-fabricate a cold arm, I report **warm-only**. (Building the cold arm would need
-the supervised agent + live control channel to actually drive the gorilla ship,
-plus aging the warm window out — out of scope for a clean, real measurement
-here.)
+## Cold-fallback arm (MEASURED) — cold/archive-answered PromQL
+
+This is the arm that was previously blocked. It is now measured end-to-end
+through the gorilla cold tier.
+
+### Setup (cold-ON stack, `datasets_eval/latency/stack-coldon.sh`)
+
+Full cold stack on one host: **MinIO** (:9000) + **gorilla-merger**
+(:10908 HTTP ingest / :10907 Thanos StoreAPI) + **thanos-store-gateway**
+(reads the merger's shipped `asap-gorilla-tsdb` blocks) + **thanos-query**
+(:10903, federates the merger StoreAPI + store-gateway) + **data-plane** with
+`ASAP_THANOS_QUERY_URL=http://thanos-query:10903` (so it registers the real
+**`ThanosQueryEngine`**, `data_source_id=thanos_query`, NOT the
+`NoDataArchiveEngine` stub) + a **cold-enabled edge** with a *complete* cold
+block (`agent-cold-ship.yaml`: `cold.enabled:true`,
+`cold.ship_endpoint: http://gorilla-merger:10908/ingest/gorilla`,
+`block_duration:60s`, `external_labels.cluster:asap-mvp`).
+
+- **Routing:** a cold storage-routing table
+  (`backend-storage-routing-coldon.yaml`) pins
+  `google_cluster_2019_cpu_rate → gorilla_object_store` for every query shape,
+  so its instant queries dispatch through the `EngineRouter` to
+  `thanos_query` (the cold/archive engine) rather than the warm
+  `SketchStore`.
+- **Workload:** same `/tmp/dd-only.jsonl` slice (lossless raw
+  `google_cluster_2019_cpu_rate` Sum family, `tier: both` — the series that
+  ships cold), wall-clock-anchored replay (one cold window at a fixed instant).
+- **Control-plane stopped during timing:** it periodically re-POSTs a
+  storage-routing table to `/api/v1/storage_routing` (~every 60 s) that
+  overrides the file-loaded cold table back to warm; with it stopped, the
+  data-plane keeps the file cold table and the cold archive answers
+  independently (cold ship is decoupled from the control channel — PR #500).
+
+### What was the original blocker (and the fix)
+
+The original cold arm failed because the multisketch `agent-ddsketch-coldon.yaml`
+set only `cold: {enabled: true}` with **no `ship_endpoint`** — and the
+asapedge processor treats an empty ship endpoint as **drain-only, no
+shipping** (`config.go`: "Empty => drain-only (no shipping)"). So the cold
+encoder accumulated samples but never POSTed them → MinIO/merger stayed empty.
+With a complete cold block the edge ships per-shard ASAPFRG1 fragment batches
+to the merger. Verified live (per-shard agent logs):
+`cold drain {active_series:245, fragments:245}` → `cold shipBatch
+{fragments:245, shipper_noop:false}` with **no** ship-failure/spool lines, and
+the merger then served `count(...)=1000` over its StoreAPI.
+
+### GUARD (ran before any timing — PASSED, at the pinned cold anchor)
+
+| query | status | result | data_source |
+|---|---|---|---|
+| `sum(google_cluster_2019_cpu_rate)` | success | **22.13** (exact archive sum) | **`thanos_query`** |
+| `quantile_over_time(0.99, …_cpu_rate[300s])` | success | **1000 series** | **`thanos_query`** |
+| `quantile_over_time(0.50, …_cpu_rate[300s])` | success | 1000 series | **`thanos_query`** |
+| `count(google_cluster_2019_cpu_rate)` | success | 1000 | **`thanos_query`** |
+
+Every required timed query returns a **real archive value** served by the
+**cold engine** (`thanos_query`), so the cold timing is meaningful and is the
+cold tier (not a warm shortcut). The `quantile_over_time` is computed by
+thanos-query **over the raw archived Gorilla-XOR samples** (not over a
+DDSketch — the cold tier stores lossless raw samples).
+
+### Measurement
+
+`cold_latency_replay.py` fired the cold mix at a **fixed 15 QPS** against
+`:9091/api/v1/query`, **pinning the PromQL eval timestamp** (`time=<anchor>`)
+to the instant the cold workload was anchored at (the cold window sits at a
+fixed past instant because the ship takes ~one window to land; the warm arm
+queried live `now` because its in-memory warm window sat at `now`). The pin
+changes only WHICH timestamp the backend evaluates at — the per-query
+server-side latency it measures is identical in kind to the warm arm.
+
+- **Query mix:** `quantile_over_time(0.99,…_cpu_rate[300s])`,
+  `quantile_over_time(0.50,…_cpu_rate[300s])`, `sum(cpu_rate)`
+  (`queries-latency-cold.json`).
+- **QPS / count / duration:** 15 QPS, **600 queries**, 40 s window.
+- **Realness:** **600/600 success, 600/600 non-empty, 600/600
+  `data_source=thanos_query`** (0 empties, 0 errors, 0 warm shortcuts).
+
+### Latency table (cold-fallback archive tier)
+
+| query kind | p50 (ms) | p95 (ms) | p99 (ms) | n | note |
+|---|---|---|---|---|---|
+| **all (mix)** | **22.28** | **47.17** | **67.07** | 600 | mean 25.57, min 9.12, max 85.14 |
+| `quantile_over_time` (1000-series, Thanos PromQL over raw samples) | 24.27 | 48.59 | **68.41** | 400 | |
+| `sum` (lossless, archive) | 15.48 | 33.33 | **42.74** | 200 | exact archive sum |
+
+**Warm vs cold:** overall **p50 18.25 → 22.28 ms (≈1.2×)**, **p99 20.03 →
+67.07 ms (≈3.3×)**. The cold path stays in the tens of ms (no order-of-magnitude
+blowup) but has a heavier p99 tail: each cold answer crosses data-plane →
+thanos-query → gorilla-merger StoreAPI + store-gateway and re-evaluates PromQL
+over the raw archived samples, vs the warm tier's in-memory sketch read.
 
 ---
 
 ## Honesty / caveats
 
-- **Single-node loopback:** data-plane, agent, and replay client all on
-  `127.0.0.1`. **No network RTT** is included — these are server-side query
-  latencies only; a remote client adds its own RTT on top.
-- **QPS / count / window:** 15 QPS, 599 queries, one 40 s steady window inside
-  a single fully-shipped warm window. Modest QPS — this measures per-query
-  serving latency, not a saturation/throughput study.
-- **Warm-only:** the cold-fallback arm did not produce archived data (above);
-  no cold numbers are claimed, so the "cold ≤ 2× warm" check was not evaluated.
-- **Query realness verified:** every timed query returned a real warm value
-  (`data_source=asap_query`); the reducer hard-fails if any timed query were
-  empty/errored, so these latencies are not "latency of No result".
+- **Single-node loopback:** data-plane, agent, merger, thanos, MinIO, and the
+  replay client all on `127.0.0.1`. **No network RTT** is included — these are
+  server-side query latencies only; a remote client adds its own RTT on top.
+  (For the cold arm the inter-service hops data-plane→thanos→merger are still
+  loopback, so a real multi-host deploy would add per-hop RTT to the cold tail.)
+- **QPS / count / window:** 15 QPS; 599 (warm) / 600 (cold) queries; one 40 s
+  steady window inside a single fully-shipped window. Modest QPS — this measures
+  per-query serving latency, not a saturation/throughput study.
+- **Cold eval-time pin:** cold queries are evaluated at the fixed cold-window
+  anchor (the data sits at one instant after a wall-clock-anchored replay), so
+  the cold latencies are the backend's time to *serve a cold/archive query*,
+  not a study of cold-window freshness/aging.
+- **Query realness + tier verified:** the reducer hard-fails if any timed query
+  was empty/errored OR served by the wrong tier — warm must be
+  `data_source=asap_query`, cold must be `data_source=thanos_query` — so these
+  latencies are neither "latency of No result" nor a warm answer mislabeled as
+  cold.
 
 ---
 
@@ -161,15 +233,60 @@ python3 datasets_eval/latency/compute_latency.py --warm datasets_eval/latency/re
 bash datasets_eval/multisketch/stack-coldoff.sh down
 ```
 
+### Cold-fallback arm
+
+```bash
+# build the dev images if absent (buildx --load; data-plane/control-plane/
+# gorilla-merger from /mydata/ASAPQuery-backend, asap-otel via build_asap_otel.sh)
+# then bring up the COLD-ON stack (uses sudo docker; --user 0 on the merger):
+bash datasets_eval/latency/stack-coldon.sh up \
+  datasets_eval/multisketch/workloads/ddsketch.yaml \
+  datasets_eval/latency/agent-cold-ship.yaml \
+  datasets_eval/latency/backend-storage-routing-coldon.yaml
+# 1. wall-clock-anchored replay; capture the printed `now=<ms>` anchor
+python3 datasets_eval/google_cluster/run.py replay \
+  --jsonl /tmp/dd-only.jsonl --endpoint 127.0.0.1:4317 --pace-factor 0 --wall-clock-anchor
+# 2. wait until the cold ship lands: poll thanos-query / data-plane until
+#    count(google_cluster_2019_cpu_rate)@<anchor_s> returns 1000 via thanos_query
+# 3. stop the control-plane so it can't override the cold routing table back to warm:
+sudo docker rm -f asap-control-plane
+# 4. GUARD then timed cold replay @ 15 QPS, pinned to the anchor:
+python3 datasets_eval/latency/cold_latency_replay.py --target http://127.0.0.1:9091 \
+  --queries datasets_eval/latency/queries-latency-cold.json \
+  --at-time <anchor_s> --qps 15 --duration 40 \
+  --out datasets_eval/latency/replay-cold.jsonl
+# 5. reduce BOTH arms -> tables + combined CDF (hard-fails if any cold query
+#    was not data_source=thanos_query, or any warm query not asap_query):
+python3 datasets_eval/latency/compute_latency.py \
+  --warm datasets_eval/latency/per_query_latency.json \
+  --cold datasets_eval/latency/per_query_latency_cold.json \
+  --out-json datasets_eval/latency/latency_summary.json \
+  --out-png datasets_eval/latency/latency_cdf.png
+# 6. teardown
+bash datasets_eval/latency/stack-coldon.sh down
+```
+
 ## Deliverables (this dir)
 
 - `latency_RESULTS.md` — this file
-- `latency_cdf.png` — Fig 7 CDF (warm, overall + per-kind)
-- `latency_summary.json` — p50/p95/p99 (overall + per-kind), data_source counts
-- `per_query_latency.json` — slim per-query latency log (599 records: ts, query,
-  kind, duration_ms, status, data_source, n_result_series). The raw
-  `replay-warm.jsonl` (42 MB, verbatim 691-series result vectors per query) is
-  the reducer's input but is intentionally **not committed** — regenerate via
-  step 4 of Reproduce.
+- `latency_cdf.png` — Fig 7 CDF (warm + cold-fallback, overall + per-kind)
+- `latency_summary.json` — p50/p95/p99 (overall + per-kind), data_source counts,
+  for BOTH the `warm` and `cold` arms
+- `per_query_latency.json` — slim warm per-query log (599 records)
+- `per_query_latency_cold.json` — slim cold per-query log (600 records: ts,
+  query, kind, duration_ms, status, http_code, data_source, n_result_series)
+- `compute_latency.py` — reducer (warm + cold; renders the combined CDF; guards
+  each arm's `data_source`)
+- `cold_latency_replay.py` — cold-arm replay client (eval-time-pinned)
+- `stack-coldon.sh` — cold-ON single-host stack (MinIO + merger + thanos +
+  cold-routed data-plane + cold-enabled edge)
+- `backend-storage-routing-coldon.yaml` — cold routing table
+  (`cpu_rate → gorilla_object_store`)
+- `agent-cold-ship.yaml` — cold-enabled edge with a complete `cold.ship_endpoint`
+  block (the missing piece that unblocked the arm)
+- `queries-latency-cold.json` — the timed cold mix
+- The raw `replay-warm.jsonl` / `replay-cold.jsonl` (verbatim result vectors) are
+  the reducer's optional input but intentionally **not committed** — regenerate
+  via Reproduce.
 - `compute_latency.py` — reducer (also renders the CDF)
 - `../../deploy/mvp-singlenode/scripts/queries-latency-warm.json` — the timed mix
diff --git a/datasets_eval/latency/latency_cdf.png b/datasets_eval/latency/latency_cdf.png
index 0ac96465c..864b24d22 100644
Binary files a/datasets_eval/latency/latency_cdf.png and b/datasets_eval/latency/latency_cdf.png differ
diff --git a/datasets_eval/latency/latency_summary.json b/datasets_eval/latency/latency_summary.json
index 8506d0980..16fdfb089 100644
--- a/datasets_eval/latency/latency_summary.json
+++ b/datasets_eval/latency/latency_summary.json
@@ -12,7 +12,7 @@
       "p50_ms": 18.25,
       "p95_ms": 19.451,
       "p99_ms": 20.032,
-      "max_ms": 23.778,
+      "max_ms": 23.777,
       "mean_ms": 12.959
     },
     "by_kind": {
@@ -22,7 +22,7 @@
         "p50_ms": 18.345,
         "p95_ms": 19.564,
         "p99_ms": 20.647,
-        "max_ms": 23.778,
+        "max_ms": 23.777,
         "mean_ms": 18.569
       },
       "sum": {
@@ -35,5 +35,42 @@
         "mean_ms": 1.683
       }
     }
+  },
+  "cold": {
+    "label": "cold",
+    "n_queries": 600,
+    "n_real_warm": 600,
+    "data_source_counts": {
+      "thanos_query": 600
+    },
+    "overall": {
+      "n": 600,
+      "min_ms": 9.124,
+      "p50_ms": 22.278,
+      "p95_ms": 47.173,
+      "p99_ms": 67.071,
+      "max_ms": 85.143,
+      "mean_ms": 25.569
+    },
+    "by_kind": {
+      "quantile": {
+        "n": 400,
+        "min_ms": 16.025,
+        "p50_ms": 24.271,
+        "p95_ms": 48.592,
+        "p99_ms": 68.408,
+        "max_ms": 85.143,
+        "mean_ms": 29.433
+      },
+      "sum": {
+        "n": 200,
+        "min_ms": 9.124,
+        "p50_ms": 15.479,
+        "p95_ms": 33.333,
+        "p99_ms": 42.737,
+        "max_ms": 45.786,
+        "mean_ms": 17.843
+      }
+    }
   }
 }
diff --git a/datasets_eval/latency/per_query_latency_cold.json b/datasets_eval/latency/per_query_latency_cold.json
new file mode 100644
index 000000000..a6b9087fd
--- /dev/null
+++ b/datasets_eval/latency/per_query_latency_cold.json
@@ -0,0 +1,6002 @@
+[
+ {
+  "ts": "2026-06-14T19:53:02.474410+00:00",
+  "query": "quantile_over_time(0.99, google_cluster_2019_cpu_rate[300s])",
+  "kind": "quantile",
+  "duration_ms": 34.3306,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1000
+ },
+ {
+  "ts": "2026-06-14T19:53:02.537868+00:00",
+  "query": "quantile_over_time(0.50, google_cluster_2019_cpu_rate[300s])",
+  "kind": "quantile",
+  "duration_ms": 31.8044,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1000
+ },
+ {
+  "ts": "2026-06-14T19:53:02.588112+00:00",
+  "query": "sum(google_cluster_2019_cpu_rate)",
+  "kind": "sum",
+  "duration_ms": 16.4205,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1
+ },
+ {
+  "ts": "2026-06-14T19:53:02.666114+00:00",
+  "query": "quantile_over_time(0.99, google_cluster_2019_cpu_rate[300s])",
+  "kind": "quantile",
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+ },
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+  "query": "quantile_over_time(0.50, google_cluster_2019_cpu_rate[300s])",
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+  "status": "success",
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+  "n_result_series": 1000
+ },
+ {
+  "ts": "2026-06-14T19:53:02.794125+00:00",
+  "query": "sum(google_cluster_2019_cpu_rate)",
+  "kind": "sum",
+  "duration_ms": 22.4749,
+  "status": "success",
+  "http_code": 200,
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+ },
+ {
+  "ts": "2026-06-14T19:53:02.858351+00:00",
+  "query": "quantile_over_time(0.99, google_cluster_2019_cpu_rate[300s])",
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+  "status": "success",
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+ },
+ {
+  "ts": "2026-06-14T19:53:02.981564+00:00",
+  "query": "sum(google_cluster_2019_cpu_rate)",
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+  "status": "success",
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+  "query": "quantile_over_time(0.99, google_cluster_2019_cpu_rate[300s])",
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+  "duration_ms": 18.7241,
+  "status": "success",
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+  "n_result_series": 1000
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+ {
+  "ts": "2026-06-14T19:53:03.194750+00:00",
+  "query": "sum(google_cluster_2019_cpu_rate)",
+  "kind": "sum",
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+  "status": "success",
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+  "query": "sum(google_cluster_2019_cpu_rate)",
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+ },
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+  "status": "success",
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+  "query": "quantile_over_time(0.50, google_cluster_2019_cpu_rate[300s])",
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+  "status": "success",
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+  "ts": "2026-06-14T19:53:03.986471+00:00",
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+  "status": "success",
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+  "ts": "2026-06-14T19:53:04.326505+00:00",
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+  "status": "success",
+  "http_code": 200,
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+  "n_result_series": 1000
+ },
+ {
+  "ts": "2026-06-14T19:53:04.394196+00:00",
+  "query": "sum(google_cluster_2019_cpu_rate)",
+  "kind": "sum",
+  "duration_ms": 22.5504,
+  "status": "success",
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+  "data_source": "thanos_query",
+  "n_result_series": 1
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+ {
+  "ts": "2026-06-14T19:53:04.460118+00:00",
+  "query": "quantile_over_time(0.99, google_cluster_2019_cpu_rate[300s])",
+  "kind": "quantile",
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+  "status": "success",
+  "http_code": 200,
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+  "n_result_series": 1000
+ },
+ {
+  "ts": "2026-06-14T19:53:04.536128+00:00",
+  "query": "quantile_over_time(0.50, google_cluster_2019_cpu_rate[300s])",
+  "kind": "quantile",
+  "duration_ms": 29.6986,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1000
+ },
+ {
+  "ts": "2026-06-14T19:53:04.583341+00:00",
+  "query": "sum(google_cluster_2019_cpu_rate)",
+  "kind": "sum",
+  "duration_ms": 11.6913,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1
+ },
+ {
+  "ts": "2026-06-14T19:53:04.669967+00:00",
+  "query": "quantile_over_time(0.99, google_cluster_2019_cpu_rate[300s])",
+  "kind": "quantile",
+  "duration_ms": 30.1986,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1000
+ },
+ {
+  "ts": "2026-06-14T19:53:04.742110+00:00",
+  "query": "quantile_over_time(0.50, google_cluster_2019_cpu_rate[300s])",
+  "kind": "quantile",
+  "duration_ms": 27.6157,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1000
+ },
+ {
+  "ts": "2026-06-14T19:53:04.784227+00:00",
+  "query": "sum(google_cluster_2019_cpu_rate)",
+  "kind": "sum",
+  "duration_ms": 12.5632,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1
+ },
+ {
+  "ts": "2026-06-14T19:53:04.859449+00:00",
+  "query": "quantile_over_time(0.99, google_cluster_2019_cpu_rate[300s])",
+  "kind": "quantile",
+  "duration_ms": 19.938,
+  "status": "success",
+  "http_code": 200,
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+  "kind": "quantile",
+  "duration_ms": 22.5458,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1000
+ },
+ {
+  "ts": "2026-06-14T19:53:40.385672+00:00",
+  "query": "sum(google_cluster_2019_cpu_rate)",
+  "kind": "sum",
+  "duration_ms": 14.0153,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1
+ },
+ {
+  "ts": "2026-06-14T19:53:40.470974+00:00",
+  "query": "quantile_over_time(0.99, google_cluster_2019_cpu_rate[300s])",
+  "kind": "quantile",
+  "duration_ms": 31.2871,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1000
+ },
+ {
+  "ts": "2026-06-14T19:53:40.529411+00:00",
+  "query": "quantile_over_time(0.50, google_cluster_2019_cpu_rate[300s])",
+  "kind": "quantile",
+  "duration_ms": 23.3538,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1000
+ },
+ {
+  "ts": "2026-06-14T19:53:40.581754+00:00",
+  "query": "sum(google_cluster_2019_cpu_rate)",
+  "kind": "sum",
+  "duration_ms": 10.1003,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1
+ },
+ {
+  "ts": "2026-06-14T19:53:40.673753+00:00",
+  "query": "quantile_over_time(0.99, google_cluster_2019_cpu_rate[300s])",
+  "kind": "quantile",
+  "duration_ms": 34.2576,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1000
+ },
+ {
+  "ts": "2026-06-14T19:53:40.729994+00:00",
+  "query": "quantile_over_time(0.50, google_cluster_2019_cpu_rate[300s])",
+  "kind": "quantile",
+  "duration_ms": 23.5305,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1000
+ },
+ {
+  "ts": "2026-06-14T19:53:40.783575+00:00",
+  "query": "sum(google_cluster_2019_cpu_rate)",
+  "kind": "sum",
+  "duration_ms": 11.9238,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1
+ },
+ {
+  "ts": "2026-06-14T19:53:40.860873+00:00",
+  "query": "quantile_over_time(0.99, google_cluster_2019_cpu_rate[300s])",
+  "kind": "quantile",
+  "duration_ms": 21.475,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1000
+ },
+ {
+  "ts": "2026-06-14T19:53:40.927323+00:00",
+  "query": "quantile_over_time(0.50, google_cluster_2019_cpu_rate[300s])",
+  "kind": "quantile",
+  "duration_ms": 21.2768,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1000
+ },
+ {
+  "ts": "2026-06-14T19:53:40.982203+00:00",
+  "query": "sum(google_cluster_2019_cpu_rate)",
+  "kind": "sum",
+  "duration_ms": 10.5513,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1
+ },
+ {
+  "ts": "2026-06-14T19:53:41.072139+00:00",
+  "query": "quantile_over_time(0.99, google_cluster_2019_cpu_rate[300s])",
+  "kind": "quantile",
+  "duration_ms": 32.2963,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1000
+ },
+ {
+  "ts": "2026-06-14T19:53:41.124876+00:00",
+  "query": "quantile_over_time(0.50, google_cluster_2019_cpu_rate[300s])",
+  "kind": "quantile",
+  "duration_ms": 18.7905,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1000
+ },
+ {
+  "ts": "2026-06-14T19:53:41.183345+00:00",
+  "query": "sum(google_cluster_2019_cpu_rate)",
+  "kind": "sum",
+  "duration_ms": 11.6922,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1
+ },
+ {
+  "ts": "2026-06-14T19:53:41.265108+00:00",
+  "query": "quantile_over_time(0.99, google_cluster_2019_cpu_rate[300s])",
+  "kind": "quantile",
+  "duration_ms": 25.4024,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1000
+ },
+ {
+  "ts": "2026-06-14T19:53:41.328141+00:00",
+  "query": "quantile_over_time(0.50, google_cluster_2019_cpu_rate[300s])",
+  "kind": "quantile",
+  "duration_ms": 22.0479,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1000
+ },
+ {
+  "ts": "2026-06-14T19:53:41.382301+00:00",
+  "query": "sum(google_cluster_2019_cpu_rate)",
+  "kind": "sum",
+  "duration_ms": 10.6386,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1
+ },
+ {
+  "ts": "2026-06-14T19:53:41.462986+00:00",
+  "query": "quantile_over_time(0.99, google_cluster_2019_cpu_rate[300s])",
+  "kind": "quantile",
+  "duration_ms": 23.5151,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1000
+ },
+ {
+  "ts": "2026-06-14T19:53:41.530012+00:00",
+  "query": "quantile_over_time(0.50, google_cluster_2019_cpu_rate[300s])",
+  "kind": "quantile",
+  "duration_ms": 23.6294,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1000
+ },
+ {
+  "ts": "2026-06-14T19:53:41.585984+00:00",
+  "query": "sum(google_cluster_2019_cpu_rate)",
+  "kind": "sum",
+  "duration_ms": 14.3346,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1
+ },
+ {
+  "ts": "2026-06-14T19:53:41.665901+00:00",
+  "query": "quantile_over_time(0.99, google_cluster_2019_cpu_rate[300s])",
+  "kind": "quantile",
+  "duration_ms": 26.4577,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1000
+ },
+ {
+  "ts": "2026-06-14T19:53:41.737548+00:00",
+  "query": "quantile_over_time(0.50, google_cluster_2019_cpu_rate[300s])",
+  "kind": "quantile",
+  "duration_ms": 31.4311,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1000
+ },
+ {
+  "ts": "2026-06-14T19:53:41.812096+00:00",
+  "query": "sum(google_cluster_2019_cpu_rate)",
+  "kind": "sum",
+  "duration_ms": 40.4347,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1
+ },
+ {
+  "ts": "2026-06-14T19:53:41.877704+00:00",
+  "query": "quantile_over_time(0.99, google_cluster_2019_cpu_rate[300s])",
+  "kind": "quantile",
+  "duration_ms": 37.9028,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1000
+ },
+ {
+  "ts": "2026-06-14T19:53:41.953294+00:00",
+  "query": "quantile_over_time(0.50, google_cluster_2019_cpu_rate[300s])",
+  "kind": "quantile",
+  "duration_ms": 47.2284,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1000
+ },
+ {
+  "ts": "2026-06-14T19:53:42.005502+00:00",
+  "query": "sum(google_cluster_2019_cpu_rate)",
+  "kind": "sum",
+  "duration_ms": 33.8497,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1
+ },
+ {
+  "ts": "2026-06-14T19:53:42.081451+00:00",
+  "query": "quantile_over_time(0.99, google_cluster_2019_cpu_rate[300s])",
+  "kind": "quantile",
+  "duration_ms": 42.014,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1000
+ },
+ {
+  "ts": "2026-06-14T19:53:42.145826+00:00",
+  "query": "quantile_over_time(0.50, google_cluster_2019_cpu_rate[300s])",
+  "kind": "quantile",
+  "duration_ms": 39.3753,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1000
+ },
+ {
+  "ts": "2026-06-14T19:53:42.205463+00:00",
+  "query": "sum(google_cluster_2019_cpu_rate)",
+  "kind": "sum",
+  "duration_ms": 33.8017,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1
+ },
+ {
+  "ts": "2026-06-14T19:53:42.277622+00:00",
+  "query": "quantile_over_time(0.99, google_cluster_2019_cpu_rate[300s])",
+  "kind": "quantile",
+  "duration_ms": 38.1789,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1000
+ },
+ {
+  "ts": "2026-06-14T19:53:42.349490+00:00",
+  "query": "quantile_over_time(0.50, google_cluster_2019_cpu_rate[300s])",
+  "kind": "quantile",
+  "duration_ms": 43.3868,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1000
+ },
+ {
+  "ts": "2026-06-14T19:53:42.397455+00:00",
+  "query": "sum(google_cluster_2019_cpu_rate)",
+  "kind": "sum",
+  "duration_ms": 25.8021,
+  "status": "success",
+  "http_code": 200,
+  "data_source": "thanos_query",
+  "n_result_series": 1
+ }
+]
\ No newline at end of file
diff --git a/datasets_eval/latency/queries-latency-cold.json b/datasets_eval/latency/queries-latency-cold.json
new file mode 100644
index 000000000..07add5a56
--- /dev/null
+++ b/datasets_eval/latency/queries-latency-cold.json
@@ -0,0 +1,5 @@
+[
+  {"id": "cold-quantile-p99", "kind": "quantile", "metricsql": "quantile_over_time(0.99, google_cluster_2019_cpu_rate[300s])"},
+  {"id": "cold-quantile-p50", "kind": "quantile", "metricsql": "quantile_over_time(0.50, google_cluster_2019_cpu_rate[300s])"},
+  {"id": "cold-sum", "kind": "sum", "metricsql": "sum(google_cluster_2019_cpu_rate)"}
+]
diff --git a/datasets_eval/latency/stack-coldon.sh b/datasets_eval/latency/stack-coldon.sh
new file mode 100755
index 000000000..30ae4e16e
--- /dev/null
+++ b/datasets_eval/latency/stack-coldon.sh
@@ -0,0 +1,153 @@
+#!/usr/bin/env bash
+# stack-coldon.sh — cold-ON single-host ASAP stack for the Fig 7
+# COLD-FALLBACK latency arm. Derived from
+# datasets_eval/multisketch/stack.sh (the full cold stack), with two
+# eval-specific changes:
+#
+#   1. ALL docker commands run via `sudo docker` (the direct docker
+#      socket is permission-denied on this host; sudo docker works).
+#   2. The data-plane mounts a COLD storage-routing table
+#      (backend-storage-routing-coldon.yaml) that routes
+#      `google_cluster_2019_cpu_rate` to `gorilla_object_store`, so its
+#      PromQL queries are answered by the ThanosQueryEngine
+#      (data_source=thanos_query) over the gorilla cold tier — that is
+#      what makes the COLD arm observable end-to-end.
+#
+# Components (all --network host on node0):
+#   minio                       : 9000 / 9001
+#   thanos store-gateway        : 10901/10902
+#   gorilla-merger HTTP/gRPC    : 10908 / 10907
+#   thanos query                : 10903 / 10905
+#   data-plane OTLP ingest      : 14317/14318, query :9091  (cold ON)
+#   control-plane               : 8080 / 4320 / 4321
+#   agent (bare fused asap_edge, cold ON) OTLP receiver :4317/4318
+#
+# Usage:
+#   stack-coldon.sh up   <workload.yaml> <agent-config.yaml> <routing.yaml>
+#   stack-coldon.sh down
+#   stack-coldon.sh ps
+set -uo pipefail
+
+ROOT=/mydata/ASAPCollector
+CFG=${ROOT}/deploy/mvp-multinode/configs
+WORKDIR=/mydata/mvp-multinode
+HERE=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)
+
+H=127.0.0.1
+ADD_HOSTS=(
+  --add-host=control-plane:${H} --add-host=data-plane:${H}
+  --add-host=minio:${H} --add-host=prometheus:${H} --add-host=victoriametrics:${H}
+  --add-host=thanos-query:${H} --add-host=thanos-store-gateway:${H}
+  --add-host=thanos-compact:${H} --add-host=gorilla-merger:${H}
+  --add-host=agent-a:${H} --add-host=serf-gw:${H}
+)
+
+DR() { sudo docker run -d --restart no --network host "${ADD_HOSTS[@]}" "$@"; }
+log(){ printf '[stack-coldon %s] %s\n' "$(date +%H:%M:%S)" "$*" >&2; }
+
+down() {
+  sudo docker ps -a --format '{{.Names}}' | grep '^asap-' | xargs -r sudo docker rm -f >/dev/null 2>&1
+  log "all asap-* containers removed"
+}
+
+wipe_state() {
+  sudo rm -rf "${WORKDIR}"/data/gorilla-merger/* "${WORKDIR}"/data/sketch-persistence/* 2>/dev/null || true
+  mkdir -p "${WORKDIR}"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/data/gorilla-merger "${WORKDIR}"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/data/sketch-persistence "${WORKDIR}"/configs
+}
+
+up() {
+  local workload=$1 agentcfg=$2 routing=$3
+  down; wipe_state
+
+  mkdir -p "${WORKDIR}"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/configs/asap "${WORKDIR}"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/configs/shared
+  cp "${CFG}/shared/thanos-objstore.yaml" "${WORKDIR}/configs/shared/"
+  cp "${CFG}/asap/backend-streaming.yaml" "${WORKDIR}/configs/asap/"
+  cp "${routing}" "${WORKDIR}/configs/asap/backend-storage-routing.yaml"
+  cp "${CFG}/asap/supervisor.yaml" "${WORKDIR}/configs/asap/" 2>/dev/null || true
+  cp "${workload}" "${WORKDIR}/configs/asap/eval-workload.yaml"
+  cp "${agentcfg}" "${WORKDIR}/configs/asap/eval-agent.yaml"
+
+  log "minio up"
+  DR --name asap-minio -e MINIO_ROOT_USER=asap -e MINIO_ROOT_PASSWORD=asap-local-only \
+     minio/minio:latest server /data --console-address :9001 >/dev/null
+  sleep 4
+  sudo docker run --rm --network host "${ADD_HOSTS[@]}" --entrypoint=sh minio/mc:latest -c '
+      mc alias set asap http://minio:9000 asap asap-local-only &&
+      mc mb --ignore-existing asap/asap-gorilla &&
+      mc mb --ignore-existing asap/asap-gorilla-tsdb &&
+      mc mb --ignore-existing asap/raw' >/dev/null 2>&1 || log "minio bucket warn"
+
+  log "thanos store-gateway up"
+  DR --name asap-thanos-store-gateway --user 0 \
+     -v "${WORKDIR}/configs/shared/thanos-objstore.yaml:/etc/thanos/objstore.yaml:ro" \
+     quay.io/thanos/thanos:v0.41.0 store \
+     --objstore.config-file=/etc/thanos/objstore.yaml \
+     --http-address=0.0.0.0:10902 --grpc-address=0.0.0.0:10901 \
+     --data-dir=/tmp/thanos-store --sync-block-duration=30s >/dev/null
+
+  log "gorilla-merger up"
+  # --user 0: the merger image is distroless `nonroot` (uid 65532) but the
+  # host bind-mount /data is owned by the invoking user; run as root so the
+  # merger can mkdir /data/pending (else: "mkdir /data/pending: permission
+  # denied"). Same workaround the thanos store-gateway uses above.
+  DR --name asap-gorilla-merger --user 0 \
+     -v "${WORKDIR}/configs/shared/thanos-objstore.yaml:/etc/thanos/objstore.yaml:ro" \
+     -v "${WORKDIR}/data/gorilla-merger:/data" \
+     asap/gorilla-merger:dev \
+     --http-address=0.0.0.0:10908 --grpc-address=0.0.0.0:10907 \
+     --tsdb.path=/data --objstore.config-file=/etc/thanos/objstore.yaml \
+     --external-labels=cluster=asap-mvp,merger=m1 >/dev/null
+
+  log "thanos query up"
+  DR --name asap-thanos-query quay.io/thanos/thanos:v0.41.0 query \
+     --http-address=0.0.0.0:10903 --grpc-address=0.0.0.0:10905 \
+     --endpoint=thanos-store-gateway:10901 --endpoint=gorilla-merger:10907 \
+     --query.replica-label=replica >/dev/null
+
+  log "data-plane up (cold ON — gorilla_object_store routing, OTLP :14317, query :9091)"
+  DR --name asap-data-plane --cpus=8 \
+     -e RUST_LOG="${DP_RUST_LOG:-info}" -e ASAP_SKETCH_FAMILY=ddsketch \
+     -e ASAP_GORILLA_S3_ENDPOINT=http://minio:9000 -e ASAP_GORILLA_S3_BUCKET=asap-gorilla \
+     -e ASAP_GORILLA_S3_REGION=us-east-1 -e ASAP_GORILLA_S3_ACCESS_KEY_ID=asap \
+     -e ASAP_GORILLA_S3_SECRET_ACCESS_KEY=asap-local-only -e ASAP_GORILLA_S3_TENANT=default \
+     -e ASAP_GORILLA_S3_USE_SSL=false \
+     -e 'ASAP_GORILLA_S3_PREFIX_TEMPLATE={tenant}/{metric}/{YYYY}/{MM}/{DD}/{HH}/' \
+     -e ASAP_BACKEND_STORAGE_ROUTING=/etc/asap/backend-storage-routing.yaml \
+     -e ASAP_THANOS_QUERY_URL=http://thanos-query:10903 \
+     -v "${WORKDIR}/configs/asap/backend-streaming.yaml:/etc/asap/streaming.yaml:ro" \
+     -v "${WORKDIR}/configs/asap/backend-storage-routing.yaml:/etc/asap/backend-storage-routing.yaml:ro" \
+     -v "${WORKDIR}/data/sketch-persistence:/data/sketch-persistence" \
+     asap/data-plane:dev \
+     --streaming-config=/etc/asap/streaming.yaml --query-port=9091 \
+     --enable-otel-ingest --otel-grpc-port=14317 --otel-http-port=14318 >/dev/null
+  sleep 4
+
+  log "control-plane up (workload=$(basename "${workload}"), backend OTLP port 14317)"
+  DR --name asap-control-plane --cpus=2 \
+     -e RUST_LOG="info,controller=debug,control_plane=debug" \
+     -e USE_TYPED_STAGE_SPLIT=1 -e ASAP_EDGE_FUSED=1 \
+     -e ASAP_EDGE_BACKEND_OTLP_PORT=14317 \
+     -e CONTROLLER_ADDR=0.0.0.0:8080 -e CONTROLLER_OPAMP_ADDR=0.0.0.0:4320 \
+     -e CONTROLLER_GRPC_ADDR=0.0.0.0:4321 \
+     -e CONTROLLER_OPAMP_ENDPOINT=ws://control-plane:4320/v1/opamp \
+     -e CONTROLLER_BACKEND_ENDPOINT=http://data-plane:9091/api/v1/streaming-config \
+     -e CONTROLLER_WORKLOADS=/etc/asap/eval-workload.yaml \
+     -v "${WORKDIR}/configs/asap/eval-workload.yaml:/etc/asap/eval-workload.yaml:ro" \
+     asap/control-plane:dev >/dev/null
+  sleep 5
+
+  log "agent (bare, static fused asap_edge, cold ON) up — OTLP receiver :4317 for replay"
+  DR --name asap-agent-a --cpus=8 --hostname agent-a \
+     -e AGENT_ID=agent-a \
+     -v "${WORKDIR}/configs/asap/eval-agent.yaml:/etc/otel/config.yaml:ro" \
+     asap/asap-otel:dev --config=/etc/otel/config.yaml >/dev/null
+  sleep 6
+  log "stack up. containers:"; sudo docker ps --format '  {{.Names}}\t{{.Status}}' | grep asap- >&2
+}
+
+case "${1:-}" in
+  up)   up "${2:?need workload}" "${3:?need agentcfg}" "${4:?need routing}" ;;
+  down) down ;;
+  ps)   sudo docker ps --format '{{.Names}}\t{{.Status}}' | grep asap- ;;
+  *) echo "usage: stack-coldon.sh up <workload> <agentcfg> <routing> | down | ps" >&2; exit 2 ;;
+esac
diff --git a/datasets_eval/multisketch/agent-raw-coldoff.yaml b/datasets_eval/multisketch/agent-raw-coldoff.yaml
new file mode 100644
index 000000000..ca2eec07c
--- /dev/null
+++ b/datasets_eval/multisketch/agent-raw-coldoff.yaml
@@ -0,0 +1,40 @@
+# RAW-forward baseline agent (no asap_edge) — for C1 sketch-vs-raw wire.
+# Auto-generated per-family agent config (multisketch eval). See make_perfamily.py.
+receivers:
+  otlp:
+    protocols:
+      grpc: {endpoint: 0.0.0.0:4317, max_recv_msg_size_mib: 4096}
+      http: {endpoint: 0.0.0.0:4318}
+processors:
+  batch:
+    send_batch_size: 800
+    send_batch_max_size: 1500
+  memory_limiter: {check_interval: 1s, limit_mib: 8192, spike_limit_mib: 1024}
+  asap_edge:
+    shard_count: 12
+    window_duration: 60s
+    drop_original: true
+    max_series: 200000
+    delta_transmission: false
+    metrics:
+      - {metric: google_cluster_2019_cpu_rate, family: sum, tier: both}
+      - {metric: google_cluster_2019_memory_usage, family: sum, aggregate_by: [zone], tier: both}
+      - metric: google_cluster_2019_cpu_rate_q_ddsketch
+        family: "ddsketch"
+        tier: "warm"
+        delta_transmission: true
+    cold: {enabled: false}
+    control_channel: {enabled: false}
+exporters:
+  otlp/backend:
+    endpoint: data-plane:14317
+    tls: {insecure: true}
+    timeout: 120s
+    sending_queue: {enabled: true, num_consumers: 4, queue_size: 5000}
+service:
+  pipelines:
+    metrics: {receivers: [otlp], processors: [memory_limiter, batch], exporters: [otlp/backend]}
+  telemetry:
+    metrics:
+      level: detailed
+      readers: [{pull: {exporter: {prometheus: {host: 0.0.0.0, port: 8890}}}}]
diff --git a/datasets_eval/multisketch/agent-raw-gzip.yaml b/datasets_eval/multisketch/agent-raw-gzip.yaml
new file mode 100644
index 000000000..fb7d558ef
--- /dev/null
+++ b/datasets_eval/multisketch/agent-raw-gzip.yaml
@@ -0,0 +1,42 @@
+# RAW-forward + gzip compression baseline (b0-gzip) for C1 encoding-factor.
+# RAW-forward baseline agent (no asap_edge) — for C1 sketch-vs-raw wire.
+# Auto-generated per-family agent config (multisketch eval). See make_perfamily.py.
+receivers:
+  otlp:
+    protocols:
+      grpc: {endpoint: 0.0.0.0:4317, max_recv_msg_size_mib: 4096}
+      http: {endpoint: 0.0.0.0:4318}
+processors:
+  batch:
+    send_batch_size: 800
+    send_batch_max_size: 1500
+  memory_limiter: {check_interval: 1s, limit_mib: 8192, spike_limit_mib: 1024}
+  asap_edge:
+    shard_count: 12
+    window_duration: 60s
+    drop_original: true
+    max_series: 200000
+    delta_transmission: false
+    metrics:
+      - {metric: google_cluster_2019_cpu_rate, family: sum, tier: both}
+      - {metric: google_cluster_2019_memory_usage, family: sum, aggregate_by: [zone], tier: both}
+      - metric: google_cluster_2019_cpu_rate_q_ddsketch
+        family: "ddsketch"
+        tier: "warm"
+        delta_transmission: true
+    cold: {enabled: false}
+    control_channel: {enabled: false}
+exporters:
+  otlp/backend:
+    endpoint: data-plane:14317
+    compression: gzip
+    tls: {insecure: true}
+    timeout: 120s
+    sending_queue: {enabled: true, num_consumers: 4, queue_size: 5000}
+service:
+  pipelines:
+    metrics: {receivers: [otlp], processors: [memory_limiter, batch], exporters: [otlp/backend]}
+  telemetry:
+    metrics:
+      level: detailed
+      readers: [{pull: {exporter: {prometheus: {host: 0.0.0.0, port: 8890}}}}]
diff --git a/datasets_eval/multisketch/agent-raw-zstd.yaml b/datasets_eval/multisketch/agent-raw-zstd.yaml
new file mode 100644
index 000000000..bd9813fc1
--- /dev/null
+++ b/datasets_eval/multisketch/agent-raw-zstd.yaml
@@ -0,0 +1,42 @@
+# RAW-forward + zstd compression baseline (b0-zstd) for C1 encoding-factor.
+# RAW-forward baseline agent (no asap_edge) — for C1 sketch-vs-raw wire.
+# Auto-generated per-family agent config (multisketch eval). See make_perfamily.py.
+receivers:
+  otlp:
+    protocols:
+      grpc: {endpoint: 0.0.0.0:4317, max_recv_msg_size_mib: 4096}
+      http: {endpoint: 0.0.0.0:4318}
+processors:
+  batch:
+    send_batch_size: 800
+    send_batch_max_size: 1500
+  memory_limiter: {check_interval: 1s, limit_mib: 8192, spike_limit_mib: 1024}
+  asap_edge:
+    shard_count: 12
+    window_duration: 60s
+    drop_original: true
+    max_series: 200000
+    delta_transmission: false
+    metrics:
+      - {metric: google_cluster_2019_cpu_rate, family: sum, tier: both}
+      - {metric: google_cluster_2019_memory_usage, family: sum, aggregate_by: [zone], tier: both}
+      - metric: google_cluster_2019_cpu_rate_q_ddsketch
+        family: "ddsketch"
+        tier: "warm"
+        delta_transmission: true
+    cold: {enabled: false}
+    control_channel: {enabled: false}
+exporters:
+  otlp/backend:
+    endpoint: data-plane:14317
+    compression: zstd
+    tls: {insecure: true}
+    timeout: 120s
+    sending_queue: {enabled: true, num_consumers: 4, queue_size: 5000}
+service:
+  pipelines:
+    metrics: {receivers: [otlp], processors: [memory_limiter, batch], exporters: [otlp/backend]}
+  telemetry:
+    metrics:
+      level: detailed
+      readers: [{pull: {exporter: {prometheus: {host: 0.0.0.0, port: 8890}}}}]
diff --git a/datasets_eval/multisketch/baselines.py b/datasets_eval/multisketch/baselines.py
new file mode 100644
index 000000000..7a29e24c6
--- /dev/null
+++ b/datasets_eval/multisketch/baselines.py
@@ -0,0 +1,97 @@
+# LIMITATION FOUND: --network host makes lo carry BOTH the replay→agent leg
+# (constant raw ~35MB) AND the agent→data-plane leg, so lo cannot isolate the
+# wire — all arms measured ~35MB. The clean compression measurement needs the
+# CLUSTER (per-node NIC isolates agent→backend). Encoding factor was instead
+# measured offline via gzip of the payload (12.3x). See evaluation-plan (c‴).
+#!/usr/bin/env python3
+"""C1 encoding-factor baselines: ASAP-sketch vs raw-OTLP vs raw+gzip vs raw+zstd,
+on the SAME real-gct family slice.
+
+The cold-off stack is `--network host`, so per-container RX isn't isolatable;
+instead we read the loopback interface byte counter (`/proc/net/dev` lo) around
+each replay. The replay (tens of MB) dominates the small control/query overhead,
+so the delta is the actual on-the-wire bytes — and it reflects exporter
+compression (gzip/zstd), which the serialized `:8890` metric does not.
+
+Decomposition the paper wants:
+  aggregation_factor  = W_raw / W_sketch          (sketching, no compression)
+  encoding_factor     = W_raw / W_raw_gzip        (compression alone)
+  net ASAP vs raw+gzip = W_raw_gzip / W_sketch     (the honest baseline)
+"""
+import json, math, subprocess, sys, time
+from pathlib import Path
+
+HERE = Path(__file__).resolve().parent
+ROOT = HERE.parents[2]
+GCT = HERE.parent / "google_cluster"
+STACK = str(HERE / "stack-coldoff.sh")
+SLICE = "/tmp/perfam-ddsketch.jsonl"  # set by c1_wire.slice_family before this runs
+
+ARMS = {
+    "raw":      "agent-raw-coldoff.yaml",
+    "raw_gzip": "agent-raw-gzip.yaml",
+    "raw_zstd": "agent-raw-zstd.yaml",
+    "sketch":   "agent-ddsketch-coldoff.yaml",
+}
+
+
+def lo_rx_bytes():
+    for line in Path("/proc/net/dev").read_text().splitlines():
+        if line.strip().startswith("lo:"):
+            return int(line.split(":")[1].split()[0])
+    return 0
+
+
+def replay(jsonl):
+    subprocess.call([sys.executable, str(GCT / "run.py"), "replay",
+                     "--jsonl", jsonl, "--endpoint", "127.0.0.1:4317",
+                     "--pace-factor", "0", "--wall-clock-anchor"])
+
+
+def run_arm(agent, settle=35):
+    subprocess.run([STACK, "down"], cwd=str(ROOT), timeout=60)
+    subprocess.run([STACK, "up", str(HERE / "workloads" / "ddsketch.yaml"),
+                    str(HERE / agent)], cwd=str(ROOT), check=True, timeout=180)
+    time.sleep(3)
+    b0 = lo_rx_bytes()
+    replay(SLICE)
+    time.sleep(settle)  # let the export queue + sketch ship fully
+    wire = lo_rx_bytes() - b0
+    subprocess.run([STACK, "down"], cwd=str(ROOT), timeout=60)
+    return wire
+
+
+def ci95(xs):
+    xs = [x for x in xs if x]
+    if len(xs) < 2:
+        return (xs[0] if xs else float("nan")), 0.0
+    m = sum(xs) / len(xs)
+    sd = (sum((x - m) ** 2 for x in xs) / (len(xs) - 1)) ** 0.5
+    return m, 1.96 * sd / math.sqrt(len(xs))
+
+
+def main():
+    trials = int(sys.argv[1]) if len(sys.argv) > 1 else 3
+    data = {a: [] for a in ARMS}
+    for t in range(trials):
+        for arm, agent in ARMS.items():
+            w = run_arm(agent)
+            data[arm].append(w)
+            print(f"  trial {t+1} {arm:9} lo-wire {w/1e6:8.2f} MB", file=sys.stderr)
+    means = {a: ci95(v) for a, v in data.items()}
+    out = {a: dict(wire_MB=m / 1e6, ci95_MB=c / 1e6, n=len([x for x in data[a] if x]))
+           for a, (m, c) in means.items()}
+    # factors
+    wr, ws = means["raw"][0], means["sketch"][0]
+    wg = means["raw_gzip"][0]
+    out["_factors"] = dict(
+        aggregation_raw_over_sketch=wr / ws if ws else None,
+        encoding_raw_over_gzip=wr / wg if wg else None,
+        net_asap_vs_rawgzip=wg / ws if ws else None,
+    )
+    (HERE / "results" / "baselines.json").write_text(json.dumps(out, indent=2) + "\n")
+    print(json.dumps(out, indent=2))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/datasets_eval/multisketch/c1_wire.py b/datasets_eval/multisketch/c1_wire.py
new file mode 100644
index 000000000..79693d4be
--- /dev/null
+++ b/datasets_eval/multisketch/c1_wire.py
@@ -0,0 +1,126 @@
+#!/usr/bin/env python3
+"""Clean C1 bandwidth (sketch vs raw) on real gct, with trials + 95% CI.
+
+Fixes the two harness gaps that polluted the per-family wire:
+  1. TRUE per-family data slice — keep only {cpu_rate (Sum anchor for the
+     ship-wait), memory_usage (Sum), <family sketch metric>}; drop the other
+     6 sketch aliases that were forwarded raw and dominated the 57 MB.
+  2. A RAW-forward baseline arm (agent-raw-coldoff.yaml, no asap_edge) replays
+     the SAME slice so the comparison is apples-to-apples.
+
+Per trial: sketch arm (via run_perfamily) → W_sketch + accuracy; raw arm →
+W_raw. Reduction = W_raw / W_sketch. Aggregated mean ± 95% CI over N trials.
+"""
+import json, math, subprocess, sys, time, urllib.request
+from pathlib import Path
+
+HERE = Path(__file__).resolve().parent
+ROOT = HERE.parents[2]
+GCT = HERE.parent / "google_cluster"
+STACK = str(HERE / "stack-coldoff.sh")
+SOURCE = "/tmp/perfam-source.jsonl"
+AGENT_METRICS = "http://127.0.0.1:8890/metrics"
+
+# family → its sketch metric; the slice always also keeps the two Sum anchors.
+FAM_METRIC = {
+    "ddsketch": "google_cluster_2019_cpu_rate_q_ddsketch",
+    "kll": "google_cluster_2019_cpu_rate_q_kll",
+    "hll": "google_cluster_2019_cpu_rate_card_hll",
+}
+ANCHORS = {"google_cluster_2019_cpu_rate", "google_cluster_2019_memory_usage"}
+
+
+def slice_family(fam):
+    keep = ANCHORS | {FAM_METRIC[fam]}
+    dst = f"/tmp/perfam-{fam}.jsonl"
+    n = 0
+    with open(SOURCE) as f, open(dst, "w") as o:
+        for line in f:
+            try:
+                m = json.loads(line)
+            except Exception:
+                continue
+            if m.get("metric") in keep:
+                o.write(line)
+                n += 1
+    return dst, n
+
+
+def agent_wire():
+    """(bytes, points) shipped agent→backend, from the agent's otel telemetry."""
+    wire = pts = None
+    try:
+        text = urllib.request.urlopen(AGENT_METRICS, timeout=10).read().decode()
+        for line in text.splitlines():
+            if "otlp/backend" not in line:
+                continue
+            if line.startswith("otelcol_exporter_sent_metric_points_total"):
+                pts = float(line.rsplit(" ", 1)[1])
+            elif line.startswith("otelcol_exporter_queue_batch_send_size_bytes_sum"):
+                wire = float(line.rsplit(" ", 1)[1])
+    except Exception:
+        pass
+    return wire, pts
+
+
+def run_sketch(fam):
+    """Sketch arm via run_perfamily; returns (W_sketch_bytes, accuracy_dict)."""
+    subprocess.run([sys.executable, str(HERE / "run_perfamily.py"),
+                    "--arms", fam, "--window", "120s"],
+                   cwd=str(HERE), check=False, timeout=600)
+    rec = json.loads((HERE / "results" / "perfamily-all.json").read_text()).get(fam, {})
+    return rec.get("agent_wire_bytes_to_backend"), rec.get("score", {})
+
+
+def run_raw(fam, sliced):
+    """Raw-forward arm: replay the same slice through a no-asap_edge agent."""
+    subprocess.run([STACK, "down"], cwd=str(ROOT), timeout=60)
+    subprocess.run([STACK, "up", str(HERE / "workloads" / f"{fam}.yaml"),
+                    str(HERE / "agent-raw-coldoff.yaml")],
+                   cwd=str(ROOT), check=True, timeout=180)
+    subprocess.call([sys.executable, str(GCT / "run.py"), "replay",
+                     "--jsonl", sliced, "--endpoint", "127.0.0.1:4317",
+                     "--pace-factor", "0", "--wall-clock-anchor"])
+    time.sleep(20)  # let the export queue drain
+    wire, _ = agent_wire()
+    subprocess.run([STACK, "down"], cwd=str(ROOT), timeout=60)
+    return wire
+
+
+def ci95(xs):
+    xs = [x for x in xs if x is not None]
+    if len(xs) < 2:
+        return (xs[0] if xs else float("nan")), 0.0
+    m = sum(xs) / len(xs)
+    sd = (sum((x - m) ** 2 for x in xs) / (len(xs) - 1)) ** 0.5
+    return m, 1.96 * sd / math.sqrt(len(xs))
+
+
+def main():
+    fams = (sys.argv[1].split(",") if len(sys.argv) > 1 else ["ddsketch", "hll"])
+    trials = int(sys.argv[2]) if len(sys.argv) > 2 else 3
+    out = {}
+    for fam in fams:
+        sliced, n = slice_family(fam)
+        print(f"\n##### {fam}: sliced {n} pts → {sliced}", file=sys.stderr)
+        reds, ws, wr, accs = [], [], [], []
+        for t in range(trials):
+            print(f"  [{fam}] trial {t+1}/{trials} sketch arm...", file=sys.stderr)
+            w_s, acc = run_sketch(fam)
+            print(f"  [{fam}] trial {t+1}/{trials} raw arm...", file=sys.stderr)
+            w_r = run_raw(fam, sliced)
+            if w_s and w_r and w_s > 0:
+                reds.append(w_r / w_s); ws.append(w_s); wr.append(w_r); accs.append(acc)
+            print(f"    W_sketch={w_s} W_raw={w_r} reduction={(w_r/w_s if w_s else 0):.1f}x",
+                  file=sys.stderr)
+        rm, rci = ci95(reds)
+        out[fam] = dict(trials=len(reds), reduction_mean=rm, reduction_ci95=rci,
+                        W_sketch_mean=ci95(ws)[0], W_raw_mean=ci95(wr)[0],
+                        accuracy_last=accs[-1] if accs else None)
+        print(f"##### {fam}: reduction {rm:.1f}× ±{rci:.1f} (n={len(reds)})", file=sys.stderr)
+    (HERE / "results" / "c1-wire.json").write_text(json.dumps(out, indent=2, default=str) + "\n")
+    print(json.dumps(out, indent=2, default=str))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/datasets_eval/multisketch/fig7_latency.sh b/datasets_eval/multisketch/fig7_latency.sh
new file mode 100644
index 000000000..c5a503f22
--- /dev/null
+++ b/datasets_eval/multisketch/fig7_latency.sh
@@ -0,0 +1,61 @@
+#!/usr/bin/env bash
+# fig7_latency.sh — Fig 7 cold-OFF warm-tier query latency CDF.
+#
+# Brings up the cold-OFF (warm-only) all-families stack on node0, replays the
+# aliased gct trace to populate the DDSketch warm window, then replays warm
+# latency queries (queries-latency-warm.json) against the warm tier :9091 and
+# reports p50/p99. cold OFF => no Thanos archive failover, so range quantiles
+# resolve warm (the design's ~20ms target) unlike the cold-ON multinode sweep.
+set -uo pipefail
+HERE="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+ROOT=/mydata/ASAPCollector
+GCT="${ROOT}/datasets_eval/google_cluster"
+TRACE="${TRACE:-/tmp/perfam-ddsketch.jsonl}"   # aliased trace (contains _q_ddsketch)
+QUERIES="${ROOT}/deploy/mvp-singlenode/scripts/queries-latency-warm.json"
+OUT="${OUT:-/mydata/eval/results/fig7}"; mkdir -p "${OUT}"
+log(){ printf '[%s] [fig7] %s\n' "$(date +%H:%M:%S)" "$*"; }
+
+bash "${HERE}/stack-coldoff.sh" down >/dev/null 2>&1 || true
+log "stack up (all-families cold-OFF)"
+bash "${HERE}/stack-coldoff.sh" up "${HERE}/workloads/all-families.yaml" \
+     "${HERE}/agent-allfamilies-coldoff.yaml" >"${OUT}/stack.log" 2>&1
+sleep 5
+
+log "replay aliased trace (wall-clock anchored) to populate warm DDSketch"
+python3 "${GCT}/run.py" replay --jsonl "${TRACE}" --endpoint 127.0.0.1:4317 \
+    --pace-factor 0 --wall-clock-anchor >"${OUT}/replay.log" 2>&1
+
+# wait until the warm quantile resolves (sketch sealed+queryable)
+log "waiting for warm window to seal..."
+for i in $(seq 1 30); do
+  r=$(curl -s -G "http://127.0.0.1:9091/api/v1/query" \
+       --data-urlencode "query=quantile_over_time(0.50, google_cluster_2019_cpu_rate_q_ddsketch[300s])" 2>/dev/null)
+  echo "$r" | grep -q '"result":\[{' && { log "warm queryable"; break; }
+  sleep 4
+done
+
+log "replay latency queries (60s @ 15 qps)"
+python3 "${ROOT}/deploy/mvp-singlenode/scripts/metricsql_replay.py" \
+    --target http://127.0.0.1:9091 --queries "${QUERIES}" \
+    --qps 15 --duration 60 --no-plan-poll --out "${OUT}/fig7_latency.jsonl" \
+    >"${OUT}/replay_lat.log" 2>&1 || true
+
+log "per-query latency stats"
+python3 - "${OUT}/fig7_latency.jsonl" <<'PY'
+import json, sys
+from collections import defaultdict
+by=defaultdict(list); allv=[]
+for ln in open(sys.argv[1]):
+    try:
+        d=json.loads(ln); v=d.get('duration_ms') or d.get('latency_ms')
+        if v is None: continue
+        k=d.get('kind') or d.get('id') or 'all'; by[k].append(float(v)); allv.append(float(v))
+    except: pass
+def stat(v):
+    v=sorted(v); n=len(v); return (n, v[n//2], v[min(n-1,int(n*0.99))]) if n else (0,0,0)
+for k in list(by)+['all']:
+    v=allv if k=='all' else by[k]; n,p50,p99=stat(v)
+    print(f"{k}: n={n} p50={p50:.2f} p99={p99:.2f} ms")
+PY
+bash "${HERE}/stack-coldoff.sh" down >/dev/null 2>&1 || true
+log "done -> ${OUT}/fig7_latency.jsonl"
diff --git a/datasets_eval/multisketch/fig8_placement.sh b/datasets_eval/multisketch/fig8_placement.sh
new file mode 100644
index 000000000..330fc2957
--- /dev/null
+++ b/datasets_eval/multisketch/fig8_placement.sh
@@ -0,0 +1,63 @@
+#!/usr/bin/env bash
+# fig8_placement.sh — Fig 8 cross-layer placement: same DDSketch agg_type computed
+# at the SDK vs the agent, measuring where the CPU/RSS lands across the
+# producer / agent / backend layers. Single cold-OFF stack on node0; only the
+# producer's -agg changes between arms:
+#   agent placement : producer emits raw-buffer -> the asap_edge agent sketches
+#   sdk placement   : producer emits AggregationDDSketch -> agent forwards
+set -uo pipefail
+HERE="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+ROOT=/mydata/ASAPCollector
+OUT="${OUT:-/mydata/eval/results/fig8}"; mkdir -p "${OUT}"
+CSV="${OUT}/fig8.csv"; echo "placement,layer,container,cpu_perc,rss_mib" > "${CSV}"
+log(){ printf '[%s] [fig8] %s\n' "$(date +%H:%M:%S)" "$*"; }
+
+bash "${HERE}/stack-coldoff.sh" down >/dev/null 2>&1 || true
+log "stack up (ddsketch cold-OFF)"
+bash "${HERE}/stack-coldoff.sh" up "${HERE}/workloads/ddsketch.yaml" \
+     "${HERE}/agent-ddsketch-coldoff.yaml" >"${OUT}/stack.log" 2>&1
+sleep 6
+
+# sample mean CPU/RSS of a container over ~24s (6 snapshots)
+sample(){ # sample CONTAINER
+  python3 - "$1" <<'PY'
+import subprocess,sys,re,time
+c=sys.argv[1]; cpu=[]; mem=[]
+for _ in range(6):
+    try:
+        o=subprocess.run(["docker","stats","--no-stream","--format","{{.CPUPerc}}|{{.MemUsage}}",c],
+                         capture_output=True,text=True,timeout=8).stdout.strip()
+        if "|" in o:
+            cp,mm=o.split("|"); cpu.append(float(cp.strip("% ")))
+            m=re.search(r'([\d.]+)([KMG]i?B)',mm);
+            if m: mem.append(float(m.group(1))*{"KiB":1/1024,"MiB":1,"GiB":1024,"KB":1/1024,"MB":1,"GB":1024}.get(m.group(2),1))
+    except: pass
+    time.sleep(4)
+print(f"{(sum(cpu)/len(cpu) if cpu else 0):.1f},{(sum(mem)/len(mem) if mem else 0):.0f}")
+PY
+}
+
+run_arm(){ # run_arm PLACEMENT AGG
+  local placement=$1 agg=$2
+  docker rm -f asap-prod-f8 >/dev/null 2>&1 || true
+  log "${placement}: producer -agg=${agg}"
+  docker run -d --network host --name asap-prod-f8 asap/otel-app:dev \
+    -target=127.0.0.1:4317 -producer-id=f8 -metric=http_requests_total \
+    -cardinality=500 -freq-hz=100 -sdk-window=1s -agg="${agg}" \
+    -five-sketch=false -freshness-probes=false >/dev/null
+  sleep 35   # warm + steady state
+  for pair in "producer:asap-prod-f8" "agent:asap-agent-a" "backend:asap-data-plane"; do
+    layer=${pair%%:*}; c=${pair#*:}
+    stat=$(sample "$c")
+    echo "${placement},${layer},${c},${stat}" >> "${CSV}"
+    log "  ${layer} (${c}): cpu%,rss=${stat}"
+  done
+  docker rm -f asap-prod-f8 >/dev/null 2>&1 || true
+  sleep 3
+}
+
+run_arm agent   raw-buffer
+run_arm sdk     ddsketch
+
+log "done -> ${CSV}"; column -t -s, "${CSV}"
+bash "${HERE}/stack-coldoff.sh" down >/dev/null 2>&1 || true
diff --git a/datasets_eval/multisketch/run_perfamily.py b/datasets_eval/multisketch/run_perfamily.py
index 61f627cfa..2163c77c3 100644
--- a/datasets_eval/multisketch/run_perfamily.py
+++ b/datasets_eval/multisketch/run_perfamily.py
@@ -26,7 +26,7 @@
 results/perfamily-all.json.
 """
 from __future__ import annotations
-import argparse, json, math, subprocess, sys, time, urllib.parse, urllib.request
+import argparse, json, math, os, subprocess, sys, time, urllib.parse, urllib.request
 from collections import defaultdict
 from pathlib import Path
 
@@ -36,8 +36,13 @@
 sys.path.insert(0, str(GCT / "e2e"))
 import gt_eval  # noqa: E402
 
-BASE = "http://127.0.0.1:9091"
-AGENT_METRICS = "http://127.0.0.1:8890/metrics"
+# Endpoints are env-overridable so the SAME accuracy scorers run against a
+# remote MULTINODE backend (e2e_metrics.sh sets E2E_BACKEND=node2:9091 etc.)
+# instead of the single-host stack.
+BASE = os.environ.get("E2E_BACKEND", "http://127.0.0.1:9091")
+AGENT_METRICS = os.environ.get("E2E_AGENT_METRICS", "http://127.0.0.1:8890/metrics")
+REPLAY_ENDPOINT = os.environ.get("E2E_REPLAY_ENDPOINT", "127.0.0.1:4317")
+EXTERNAL_STACK = os.environ.get("E2E_EXTERNAL_STACK") == "1"
 STACK = str(HERE / "stack-coldoff.sh")
 
 
@@ -242,17 +247,21 @@ def score_cardinality(metric, rows, t, key_label):
 
 
 def stack_up(workload, agent):
+    if EXTERNAL_STACK:  # multinode: the stack is already up (e2e_metrics.sh)
+        return
     subprocess.run([STACK, "up", str(HERE / workload), str(HERE / agent)],
                    cwd=str(ROOT), check=True, timeout=180)
 
 
 def stack_down():
+    if EXTERNAL_STACK:
+        return
     subprocess.run([STACK, "down"], cwd=str(ROOT), timeout=60)
 
 
 def replay(jsonl):
     subprocess.call([sys.executable, str(GCT / "run.py"), "replay",
-                     "--jsonl", jsonl, "--endpoint", "127.0.0.1:4317",
+                     "--jsonl", jsonl, "--endpoint", REPLAY_ENDPOINT,
                      "--pace-factor", "0", "--wall-clock-anchor"])
 
 
diff --git a/datasets_eval/soak/analyze.py b/datasets_eval/soak/analyze.py
new file mode 100644
index 000000000..fd1240180
--- /dev/null
+++ b/datasets_eval/soak/analyze.py
@@ -0,0 +1,218 @@
+#!/usr/bin/env python3
+"""analyze.py — reduce soak samples to Fig-6 stats + RSS-over-time PNG.
+
+Inputs:
+  --b0 samples_b0.jsonl       (measurement (a) raw-forward arm /proc samples)
+  --b3 samples_b3_soak.jsonl  (measurement (a)+(b) sketch arm /proc samples)
+  --memdiag dp_memdiag_b3.log (data_plane [MEMORY_DIAG] SketchStore lines)
+
+Outputs:
+  - prints the arm CPU/RSS table + slope verdicts (stdout)
+  - writes rss_over_time.png
+  - writes summary.json
+"""
+from __future__ import annotations
+
+import argparse
+import json
+import re
+import sys
+
+import numpy as np
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+
+MEMDIAG_RE = re.compile(
+    r"SketchStore: (\d+) instance.* (\d+) sid\(s\) with state, "
+    r"payload=([\d.]+) KB.*process RSS=([\d.]+) MB")
+
+
+def load_samples(path):
+    edge_t, edge_rss, edge_cpu = [], [], []
+    dp_t, dp_rss, dp_cpu = [], [], []
+    with open(path) as f:
+        for line in f:
+            line = line.strip()
+            if not line:
+                continue
+            r = json.loads(line)
+            t = r["t_rel"]
+            e = r.get("edge", {})
+            d = r.get("dp", {})
+            if e.get("alive") and e.get("rss_mb") is not None:
+                edge_t.append(t); edge_rss.append(e["rss_mb"])
+                if e.get("cpu_pct") is not None:
+                    edge_cpu.append(e["cpu_pct"])
+            if d.get("alive") and d.get("rss_mb") is not None:
+                dp_t.append(t); dp_rss.append(d["rss_mb"])
+                if d.get("cpu_pct") is not None:
+                    dp_cpu.append(d["cpu_pct"])
+    return {"edge_t": edge_t, "edge_rss": edge_rss, "edge_cpu": edge_cpu,
+            "dp_t": dp_t, "dp_rss": dp_rss, "dp_cpu": dp_cpu}
+
+
+def load_memdiag(path):
+    t, sids, payload_kb, rss_mb = [], [], [], []
+    with open(path) as f:
+        for line in f:
+            m = MEMDIAG_RE.search(line)
+            if not m:
+                continue
+            sids.append(int(m.group(2)))
+            payload_kb.append(float(m.group(3)))
+            rss_mb.append(float(m.group(4)))
+    # synthesize a 30s-cadence time axis (MEMORY_DIAG logs every 30s)
+    t = [30.0 * i for i in range(len(sids))]
+    return {"t": t, "sids": sids, "payload_kb": payload_kb, "rss_mb": rss_mb}
+
+
+def stats(cpu, rss, t):
+    if not cpu:
+        cpu = [0.0]
+    return {
+        "n": len(rss),
+        "mean_cpu_pct": round(float(np.mean(cpu)), 2),
+        "p99_cpu_pct": round(float(np.percentile(cpu, 99)), 2),
+        "steady_rss_mb": round(float(np.median(rss[len(rss)//2:])), 1) if rss else None,
+        "rss_min_mb": round(min(rss), 1) if rss else None,
+        "rss_max_mb": round(max(rss), 1) if rss else None,
+    }
+
+
+def slope_mb_per_h(t, rss, tail_frac=0.5):
+    """Linear fit of RSS(MB) vs t(s) over the tail (post warm-up); MB/hour."""
+    if len(t) < 4:
+        return None
+    i0 = int(len(t) * (1 - tail_frac))
+    tt = np.array(t[i0:]); rr = np.array(rss[i0:])
+    if tt.max() - tt.min() < 1:
+        return None
+    a, b = np.polyfit(tt, rr, 1)  # a = MB/s
+    return round(a * 3600.0, 2)
+
+
+def verdict(slope, span_h):
+    if slope is None:
+        return "indeterminate (too few samples)"
+    proj_24h = slope * 24
+    if abs(slope) < 5:
+        return f"BOUNDED (slope {slope:+.2f} MB/h ~= 0; 24h extrap {proj_24h:+.1f} MB)"
+    if slope > 0:
+        return f"CLIMBING (slope {slope:+.2f} MB/h; 24h extrap {proj_24h:+.1f} MB)"
+    return f"DECLINING (slope {slope:+.2f} MB/h)"
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--b0")
+    ap.add_argument("--b3", required=True)
+    ap.add_argument("--memdiag")
+    ap.add_argument("--png", default="rss_over_time.png")
+    ap.add_argument("--out", default="summary.json")
+    args = ap.parse_args()
+
+    b3 = load_samples(args.b3)
+    summary = {"arms": {}}
+
+    # measurement (a): first 180s window of b3 = the steady CPU/RSS comparator
+    def window(d, key_t, key_rss, key_cpu, tmax):
+        ts = d[key_t]
+        idx = [i for i, t in enumerate(ts) if t <= tmax]
+        return ([d[key_cpu][i] for i in idx if i < len(d[key_cpu])],
+                [d[key_rss][i] for i in idx], [ts[i] for i in idx])
+
+    if args.b0:
+        b0 = load_samples(args.b0)
+        c, r, t = window(b0, "edge_t", "edge_rss", "edge_cpu", 1e9)
+        summary["arms"]["b0_raw_forward_edge"] = stats(c, r, t)
+        c, r, t = window(b0, "dp_t", "dp_rss", "dp_cpu", 1e9)
+        summary["arms"]["b0_data_plane"] = stats(c, r, t)
+
+    # b3 measurement (a): use first 180s for the comparable steady CPU/RSS
+    c, r, t = window(b3, "edge_t", "edge_rss", "edge_cpu", 180)
+    summary["arms"]["b3_sketch_edge_first180s"] = stats(c, r, t)
+    c, r, t = window(b3, "dp_t", "dp_rss", "dp_cpu", 180)
+    summary["arms"]["b3_data_plane_first180s"] = stats(c, r, t)
+
+    # full-soak stats
+    summary["arms"]["b3_sketch_edge_fullsoak"] = stats(
+        b3["edge_cpu"], b3["edge_rss"], b3["edge_t"])
+    summary["arms"]["b3_data_plane_fullsoak"] = stats(
+        b3["dp_cpu"], b3["dp_rss"], b3["dp_t"])
+
+    # leak slopes (tail half, after warm-up)
+    span_h = (b3["edge_t"][-1] - b3["edge_t"][0]) / 3600.0 if b3["edge_t"] else 0
+    edge_slope = slope_mb_per_h(b3["edge_t"], b3["edge_rss"])
+    dp_slope = slope_mb_per_h(b3["dp_t"], b3["dp_rss"])
+    summary["soak"] = {
+        "duration_s": round(b3["edge_t"][-1], 1) if b3["edge_t"] else 0,
+        "duration_h": round(span_h, 3),
+        "edge_rss_slope_mb_per_h": edge_slope,
+        "edge_verdict": verdict(edge_slope, span_h),
+        "dp_rss_slope_mb_per_h": dp_slope,
+        "dp_verdict": verdict(dp_slope, span_h),
+    }
+
+    md = None
+    if args.memdiag:
+        md = load_memdiag(args.memdiag)
+        if md["sids"]:
+            md_slope = slope_mb_per_h(md["t"], md["rss_mb"])
+            summary["data_plane_memdiag"] = {
+                "n": len(md["sids"]),
+                "sids_min": min(md["sids"]), "sids_max": max(md["sids"]),
+                "sids_final": md["sids"][-1],
+                "payload_kb_final": md["payload_kb"][-1],
+                "rss_mb_min": min(md["rss_mb"]), "rss_mb_max": max(md["rss_mb"]),
+                "rss_mb_final": md["rss_mb"][-1],
+                "rss_slope_mb_per_h": md_slope,
+                "rss_verdict": verdict(md_slope, span_h),
+            }
+
+    # ---- plot ----
+    fig, axes = plt.subplots(2, 1, figsize=(10, 9), sharex=False)
+    ax = axes[0]
+    ax.plot(b3["edge_t"], b3["edge_rss"], label="b3 sketch edge (asap-otel) RSS",
+            color="C0", lw=1.4)
+    ax.plot(b3["dp_t"], b3["dp_rss"], label="b3 data_plane RSS (/proc)",
+            color="C1", lw=1.4)
+    if args.b0:
+        ax.plot(b0["edge_t"], b0["edge_rss"],
+                label="b0 raw-forward edge RSS", color="C2", lw=1.0, ls="--")
+    ax.set_ylabel("process RSS (MB)")
+    ax.set_xlabel("soak time (s)")
+    ax.set_title("Fig 6 — edge / data_plane RSS over time "
+                 f"(b3 soak {span_h:.2f} h @ 5000 pts/s, single-node loopback)")
+    ax.grid(alpha=0.3); ax.legend(loc="best", fontsize=8)
+    txt = (f"edge slope {edge_slope:+.2f} MB/h\n"
+           f"dp(/proc) slope {dp_slope:+.2f} MB/h")
+    ax.text(0.02, 0.97, txt, transform=ax.transAxes, va="top", fontsize=8,
+            bbox=dict(boxstyle="round", fc="white", alpha=0.7))
+
+    ax2 = axes[1]
+    if md and md["sids"]:
+        ax2.plot(md["t"], md["rss_mb"], color="C1", lw=1.4,
+                 label="data_plane RSS (MEMORY_DIAG)")
+        ax2b = ax2.twinx()
+        ax2b.plot(md["t"], md["sids"], color="C3", lw=1.2, ls=":",
+                  label="SketchStore sids")
+        ax2b.set_ylabel("SketchStore sids", color="C3")
+        ax2.set_ylabel("data_plane RSS (MB)", color="C1")
+        ax2.set_xlabel("soak time (s)")
+        ax2.set_title("data_plane SketchStore sid count + RSS "
+                      "(stale-sid retention check)")
+        ax2.grid(alpha=0.3)
+        ax2.legend(loc="upper left", fontsize=8)
+        ax2b.legend(loc="lower right", fontsize=8)
+    fig.tight_layout()
+    fig.savefig(args.png, dpi=110)
+
+    with open(args.out, "w") as f:
+        json.dump(summary, f, indent=2)
+    print(json.dumps(summary, indent=2))
+    print(f"\nwrote {args.png} and {args.out}", file=sys.stderr)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/datasets_eval/soak/asap-otel-agent-gct-b0.yaml b/datasets_eval/soak/asap-otel-agent-gct-b0.yaml
new file mode 100644
index 000000000..efe50ea0b
--- /dev/null
+++ b/datasets_eval/soak/asap-otel-agent-gct-b0.yaml
@@ -0,0 +1,57 @@
+# Agent config — RAW-FORWARD edge, GCT-trace b0 arm (edge soak).
+#
+# The "raw-forward (b0)" arm: a plain OTel collector pipeline with NO
+# asap_edge processor — it just batches and forwards the raw OTLP stream to
+# the same data_plane OTLP ingest the b3 sketch arm ships to. This isolates
+# the edge process cost of sketch aggregation (b3) vs pure passthrough (b0):
+# same image, same receiver, same backend endpoint, same constant load; the
+# ONLY difference is whether asap_edge is in the pipeline.
+#
+# Mirrors deploy/mvp-multinode/configs/b0/asap-otel-agent-b0-otlp-none.yaml
+# (the matched-NONE raw baseline) but (1) drops the opamp extension (bare
+# agent, like stack-coldoff runs it) and (2) points the OTLP exporter at the
+# cold-OFF data_plane (data-plane:14317) instead of VictoriaMetrics, so both
+# arms share an identical downstream.
+
+receivers:
+  otlp:
+    protocols:
+      grpc:
+        endpoint: 0.0.0.0:4317
+        max_recv_msg_size_mib: 4096
+      http:
+        endpoint: 0.0.0.0:4318
+
+processors:
+  memory_limiter:
+    check_interval: 1s
+    limit_mib: 1280
+    spike_limit_mib: 256
+  batch:
+    timeout: 1s
+    send_batch_size: 1024
+
+exporters:
+  otlp/backend:
+    endpoint: data-plane:14317
+    compression: none
+    tls:
+      insecure: true
+
+service:
+  pipelines:
+    metrics:
+      receivers: [otlp]
+      processors: [memory_limiter, batch]
+      exporters: [otlp/backend]
+  telemetry:
+    logs:
+      level: info
+    metrics:
+      level: detailed
+      readers:
+        - pull:
+            exporter:
+              prometheus:
+                host: 0.0.0.0
+                port: 8890
diff --git a/datasets_eval/soak/asap-otel-agent-gct-b3.yaml b/datasets_eval/soak/asap-otel-agent-gct-b3.yaml
new file mode 100644
index 000000000..e4cb32dd1
--- /dev/null
+++ b/datasets_eval/soak/asap-otel-agent-gct-b3.yaml
@@ -0,0 +1,76 @@
+# Agent config — FUSED asap_edge edge, GCT-trace b3 sketch arm (edge soak).
+#
+# This is the "sketch (b3): DDSketch + Sum warm path (the normal edge)" arm
+# for the §6 Fig-6 edge CPU/RSS + leak-slope soak. It is a STATIC asap_edge
+# config (bare agent, no OpAMP supervisor) that maps the two metrics the
+# Google-2019 mapper emits onto the two warm families the task names:
+#
+#   google_cluster_2019_cpu_rate      -> DDSketch (per-series quantile warm)
+#   google_cluster_2019_memory_usage  -> Sum, aggregate_by [zone] (warm Sum)
+#
+# Differences vs deploy/.../asap-otel-agent-asapedge.yaml (the MVP-demo
+# static config): (1) metric map targets the GCT metrics, not the synthetic
+# http_requests_total family; (2) cold.enabled=false (cold-OFF stack has no
+# gorilla-merger); (3) backend endpoint = data-plane:14317 (the cold-OFF
+# data_plane OTLP ingest port, matching ASAP_EDGE_BACKEND_OTLP_PORT), not
+# the controller-emit default :4317; (4) window_duration=30s for faster warm
+# turnover during the soak. All keys map 1:1 to config.go.
+
+receivers:
+  otlp:
+    protocols:
+      grpc:
+        endpoint: 0.0.0.0:4317
+        max_recv_msg_size_mib: 4096
+      http:
+        endpoint: 0.0.0.0:4318
+
+processors:
+  memory_limiter:
+    check_interval: 1s
+    limit_mib: 1280
+    spike_limit_mib: 256
+
+  asap_edge:
+    shard_count: 12
+    window_duration: 30s
+    drop_original: true
+    max_series: 100000
+    delta_transmission: false
+    metrics:
+      - metric: google_cluster_2019_cpu_rate
+        family: ddsketch
+        tier: warm
+        relative_accuracy: 0.01
+      - metric: google_cluster_2019_memory_usage
+        family: sum
+        aggregate_by: [zone]
+        tier: warm
+    cold:
+      enabled: false
+    control_channel:
+      enabled: false
+
+exporters:
+  otlp/backend:
+    endpoint: data-plane:14317
+    tls:
+      insecure: true
+
+service:
+  pipelines:
+    metrics:
+      receivers: [otlp]
+      processors: [memory_limiter, asap_edge]
+      exporters: [otlp/backend]
+  telemetry:
+    logs:
+      level: info
+    metrics:
+      level: detailed
+      readers:
+        - pull:
+            exporter:
+              prometheus:
+                host: 0.0.0.0
+                port: 8890
diff --git a/datasets_eval/soak/dp_memdiag_b3.log b/datasets_eval/soak/dp_memdiag_b3.log
new file mode 100644
index 000000000..3b854d2de
--- /dev/null
+++ b/datasets_eval/soak/dp_memdiag_b3.log
@@ -0,0 +1,390 @@
+2026-06-12T21:03:23.045721Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 512 instance(s), 512 sid(s) with state, payload=257.29 KB (evictable, flusher gauge), registry+intern≈1.16 MB (resident, not flushable), process RSS=17.8 MB
+2026-06-12T21:03:23.045771Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:03:23.045783Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:03:23.045790Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:03:23.045797Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:03:23.045804Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:03:53.046386Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=835.98 KB (evictable, flusher gauge), registry+intern≈2.59 MB (resident, not flushable), process RSS=23.0 MB
+2026-06-12T21:03:53.046438Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:03:53.046448Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:03:53.046455Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:03:53.046462Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:03:53.046469Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:04:23.045660Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=1418.32 KB (evictable, flusher gauge), registry+intern≈3.16 MB (resident, not flushable), process RSS=25.1 MB
+2026-06-12T21:04:23.045706Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:04:23.045716Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:04:23.045723Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:04:23.045730Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:04:23.045737Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:04:53.045591Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=2003.10 KB (evictable, flusher gauge), registry+intern≈3.73 MB (resident, not flushable), process RSS=26.4 MB
+2026-06-12T21:04:53.045637Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:04:53.045657Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:04:53.045665Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:04:53.045672Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:04:53.045679Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:05:23.045758Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=2589.02 KB (evictable, flusher gauge), registry+intern≈4.31 MB (resident, not flushable), process RSS=26.6 MB
+2026-06-12T21:05:23.045807Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:05:23.045817Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:05:23.045824Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:05:23.045830Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:05:23.045838Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:05:53.046495Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=3171.45 KB (evictable, flusher gauge), registry+intern≈4.87 MB (resident, not flushable), process RSS=27.1 MB
+2026-06-12T21:05:53.046541Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:05:53.046551Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:05:53.046558Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:05:53.046565Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:05:53.046572Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:06:23.045758Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=3752.02 KB (evictable, flusher gauge), registry+intern≈5.44 MB (resident, not flushable), process RSS=27.6 MB
+2026-06-12T21:06:23.045802Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:06:23.045812Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:06:23.045819Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:06:23.045826Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:06:23.045833Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:06:53.046193Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=4336.81 KB (evictable, flusher gauge), registry+intern≈6.01 MB (resident, not flushable), process RSS=28.4 MB
+2026-06-12T21:06:53.046237Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:06:53.046246Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:06:53.046254Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:06:53.046261Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:06:53.046268Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:07:23.045660Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=4922.73 KB (evictable, flusher gauge), registry+intern≈6.59 MB (resident, not flushable), process RSS=29.7 MB
+2026-06-12T21:07:23.045709Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:07:23.045719Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:07:23.045726Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:07:23.045733Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:07:23.045740Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:07:53.046074Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=5505.16 KB (evictable, flusher gauge), registry+intern≈7.15 MB (resident, not flushable), process RSS=30.2 MB
+2026-06-12T21:07:53.046120Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:07:53.046130Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:07:53.046137Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:07:53.046144Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:07:53.046151Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:08:23.046447Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=6085.73 KB (evictable, flusher gauge), registry+intern≈7.72 MB (resident, not flushable), process RSS=30.7 MB
+2026-06-12T21:08:23.046491Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:08:23.046501Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:08:23.046508Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:08:23.046515Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:08:23.046522Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:08:53.045619Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=6670.51 KB (evictable, flusher gauge), registry+intern≈8.29 MB (resident, not flushable), process RSS=31.3 MB
+2026-06-12T21:08:53.045666Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:08:53.045676Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:08:53.045683Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:08:53.045690Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:08:53.045697Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:09:23.046383Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=7256.44 KB (evictable, flusher gauge), registry+intern≈8.86 MB (resident, not flushable), process RSS=32.3 MB
+2026-06-12T21:09:23.046430Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:09:23.046440Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:09:23.046447Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:09:23.046454Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:09:23.046461Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:09:53.046652Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=7838.86 KB (evictable, flusher gauge), registry+intern≈9.43 MB (resident, not flushable), process RSS=33.3 MB
+2026-06-12T21:09:53.046697Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:09:53.046707Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:09:53.046714Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:09:53.046721Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:09:53.046728Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:10:23.045977Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=8419.44 KB (evictable, flusher gauge), registry+intern≈10.00 MB (resident, not flushable), process RSS=34.1 MB
+2026-06-12T21:10:23.046023Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:10:23.046032Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:10:23.046039Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:10:23.046046Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:10:23.046053Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:10:53.046045Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=9004.22 KB (evictable, flusher gauge), registry+intern≈10.57 MB (resident, not flushable), process RSS=34.9 MB
+2026-06-12T21:10:53.046096Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:10:53.046110Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:10:53.046122Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:10:53.046133Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:10:53.046145Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:11:23.046088Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=9590.14 KB (evictable, flusher gauge), registry+intern≈11.14 MB (resident, not flushable), process RSS=35.6 MB
+2026-06-12T21:11:23.046134Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:11:23.046143Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:11:23.046150Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:11:23.046157Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:11:23.046164Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:11:53.045720Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=10172.57 KB (evictable, flusher gauge), registry+intern≈11.71 MB (resident, not flushable), process RSS=36.2 MB
+2026-06-12T21:11:53.045766Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:11:53.045775Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:11:53.045782Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:11:53.045789Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:11:53.045796Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:12:23.046342Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=10753.14 KB (evictable, flusher gauge), registry+intern≈12.28 MB (resident, not flushable), process RSS=37.4 MB
+2026-06-12T21:12:23.046389Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:12:23.046399Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:12:23.046406Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:12:23.046413Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:12:23.046420Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:12:53.045584Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=11337.93 KB (evictable, flusher gauge), registry+intern≈12.85 MB (resident, not flushable), process RSS=38.5 MB
+2026-06-12T21:12:53.045633Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:12:53.045642Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:12:53.045649Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:12:53.045656Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:12:53.045662Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:13:23.046130Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=11923.85 KB (evictable, flusher gauge), registry+intern≈13.42 MB (resident, not flushable), process RSS=39.8 MB
+2026-06-12T21:13:23.046178Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:13:23.046187Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:13:23.046194Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:13:23.046202Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:13:23.046208Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:13:53.046572Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=12506.28 KB (evictable, flusher gauge), registry+intern≈13.99 MB (resident, not flushable), process RSS=40.0 MB
+2026-06-12T21:13:53.046626Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:13:53.046635Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:13:53.046642Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:13:53.046660Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:13:53.046667Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:14:23.045902Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=13086.85 KB (evictable, flusher gauge), registry+intern≈14.56 MB (resident, not flushable), process RSS=40.3 MB
+2026-06-12T21:14:23.045951Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:14:23.045961Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:14:23.045968Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:14:23.045975Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:14:23.045981Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:14:53.046026Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=13671.63 KB (evictable, flusher gauge), registry+intern≈15.13 MB (resident, not flushable), process RSS=40.8 MB
+2026-06-12T21:14:53.046073Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:14:53.046082Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:14:53.046089Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:14:53.046096Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:14:53.046103Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:15:23.045879Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=14257.56 KB (evictable, flusher gauge), registry+intern≈15.70 MB (resident, not flushable), process RSS=41.1 MB
+2026-06-12T21:15:23.045929Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:15:23.045939Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:15:23.045946Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:15:23.045953Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:15:23.045959Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:15:53.045916Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=14839.98 KB (evictable, flusher gauge), registry+intern≈16.27 MB (resident, not flushable), process RSS=41.6 MB
+2026-06-12T21:15:53.045962Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:15:53.045972Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:15:53.045979Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:15:53.045986Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:15:53.045993Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:16:23.046602Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=15420.56 KB (evictable, flusher gauge), registry+intern≈16.84 MB (resident, not flushable), process RSS=42.9 MB
+2026-06-12T21:16:23.046663Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:16:23.046679Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:16:23.046692Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:16:23.046705Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:16:23.046718Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:16:53.046431Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=16005.34 KB (evictable, flusher gauge), registry+intern≈17.41 MB (resident, not flushable), process RSS=43.4 MB
+2026-06-12T21:16:53.046476Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:16:53.046486Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:16:53.046493Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:16:53.046500Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:16:53.046507Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:17:23.046142Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=16591.26 KB (evictable, flusher gauge), registry+intern≈17.98 MB (resident, not flushable), process RSS=43.9 MB
+2026-06-12T21:17:23.046187Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:17:23.046197Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:17:23.046204Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:17:23.046211Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:17:23.046218Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:17:53.046188Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=17173.69 KB (evictable, flusher gauge), registry+intern≈18.55 MB (resident, not flushable), process RSS=44.7 MB
+2026-06-12T21:17:53.046234Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:17:53.046243Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:17:53.046251Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:17:53.046258Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:17:53.046265Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:18:23.046054Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=17754.26 KB (evictable, flusher gauge), registry+intern≈19.12 MB (resident, not flushable), process RSS=45.2 MB
+2026-06-12T21:18:23.046106Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:18:23.046122Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:18:23.046133Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:18:23.046145Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:18:23.046157Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:18:53.046397Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=18339.05 KB (evictable, flusher gauge), registry+intern≈19.69 MB (resident, not flushable), process RSS=45.9 MB
+2026-06-12T21:18:53.046442Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:18:53.046452Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:18:53.046459Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:18:53.046466Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:18:53.046473Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:19:23.045941Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=18924.97 KB (evictable, flusher gauge), registry+intern≈20.26 MB (resident, not flushable), process RSS=47.5 MB
+2026-06-12T21:19:23.045984Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:19:23.045994Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:19:23.046001Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:19:23.046008Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:19:23.046015Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:19:53.047235Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=19507.40 KB (evictable, flusher gauge), registry+intern≈20.83 MB (resident, not flushable), process RSS=48.5 MB
+2026-06-12T21:19:53.047297Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:19:53.047315Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:19:53.047328Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:19:53.047341Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:19:53.047353Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:20:23.045826Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=20087.97 KB (evictable, flusher gauge), registry+intern≈21.39 MB (resident, not flushable), process RSS=48.5 MB
+2026-06-12T21:20:23.045871Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:20:23.045881Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:20:23.045888Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:20:23.045895Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:20:23.045901Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:20:53.045801Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=20672.75 KB (evictable, flusher gauge), registry+intern≈21.97 MB (resident, not flushable), process RSS=49.3 MB
+2026-06-12T21:20:53.045846Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:20:53.045855Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:20:53.045862Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:20:53.045870Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:20:53.045876Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:21:23.045963Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=21258.68 KB (evictable, flusher gauge), registry+intern≈22.54 MB (resident, not flushable), process RSS=50.8 MB
+2026-06-12T21:21:23.046009Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:21:23.046019Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:21:23.046026Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:21:23.046033Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:21:23.046040Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:21:53.046353Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=21841.11 KB (evictable, flusher gauge), registry+intern≈23.11 MB (resident, not flushable), process RSS=51.9 MB
+2026-06-12T21:21:53.046398Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:21:53.046408Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:21:53.046415Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:21:53.046422Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:21:53.046431Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:22:23.046672Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=22421.68 KB (evictable, flusher gauge), registry+intern≈23.67 MB (resident, not flushable), process RSS=52.1 MB
+2026-06-12T21:22:23.046717Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:22:23.046727Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:22:23.046734Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:22:23.046741Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:22:23.046748Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:22:53.045835Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=23006.46 KB (evictable, flusher gauge), registry+intern≈24.24 MB (resident, not flushable), process RSS=52.1 MB
+2026-06-12T21:22:53.045884Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:22:53.045894Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:22:53.045901Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:22:53.045907Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:22:53.045914Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:23:23.045849Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=23592.38 KB (evictable, flusher gauge), registry+intern≈24.82 MB (resident, not flushable), process RSS=53.8 MB
+2026-06-12T21:23:23.045893Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:23:23.045902Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:23:23.045909Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:23:23.045916Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:23:23.045923Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:23:53.045942Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=24174.81 KB (evictable, flusher gauge), registry+intern≈25.39 MB (resident, not flushable), process RSS=55.1 MB
+2026-06-12T21:23:53.045991Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:23:53.046000Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:23:53.046007Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:23:53.046014Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:23:53.046021Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:24:23.046264Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=24755.39 KB (evictable, flusher gauge), registry+intern≈25.95 MB (resident, not flushable), process RSS=56.1 MB
+2026-06-12T21:24:23.046317Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:24:23.046327Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:24:23.046334Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:24:23.046341Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:24:23.046347Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:24:53.046082Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=25340.17 KB (evictable, flusher gauge), registry+intern≈26.52 MB (resident, not flushable), process RSS=56.4 MB
+2026-06-12T21:24:53.046128Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:24:53.046137Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:24:53.046144Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:24:53.046151Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:24:53.046158Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:25:23.046022Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=25926.09 KB (evictable, flusher gauge), registry+intern≈27.10 MB (resident, not flushable), process RSS=57.1 MB
+2026-06-12T21:25:23.046069Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:25:23.046079Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:25:23.046087Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:25:23.046093Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:25:23.046100Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:25:53.045987Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=26508.52 KB (evictable, flusher gauge), registry+intern≈27.66 MB (resident, not flushable), process RSS=57.4 MB
+2026-06-12T21:25:53.046036Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:25:53.046046Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:25:53.046053Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:25:53.046060Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:25:53.046067Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:26:23.046005Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=27089.09 KB (evictable, flusher gauge), registry+intern≈28.23 MB (resident, not flushable), process RSS=57.7 MB
+2026-06-12T21:26:23.046053Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:26:23.046063Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:26:23.046070Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:26:23.046077Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:26:23.046084Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:26:53.046198Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=27673.88 KB (evictable, flusher gauge), registry+intern≈28.80 MB (resident, not flushable), process RSS=58.2 MB
+2026-06-12T21:26:53.046243Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:26:53.046252Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:26:53.046259Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:26:53.046266Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:26:53.046273Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:27:23.046672Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=28259.80 KB (evictable, flusher gauge), registry+intern≈29.38 MB (resident, not flushable), process RSS=58.9 MB
+2026-06-12T21:27:23.046718Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:27:23.046727Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:27:23.046734Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:27:23.046741Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:27:23.046749Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:27:53.046087Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=28842.23 KB (evictable, flusher gauge), registry+intern≈29.94 MB (resident, not flushable), process RSS=60.5 MB
+2026-06-12T21:27:53.046134Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:27:53.046143Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:27:53.046151Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:27:53.046157Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:27:53.046164Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:28:23.046051Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=29422.80 KB (evictable, flusher gauge), registry+intern≈30.51 MB (resident, not flushable), process RSS=60.8 MB
+2026-06-12T21:28:23.046096Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:28:23.046106Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:28:23.046113Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:28:23.046120Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:28:23.046127Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:28:53.046365Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=30007.58 KB (evictable, flusher gauge), registry+intern≈31.08 MB (resident, not flushable), process RSS=61.0 MB
+2026-06-12T21:28:53.046410Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:28:53.046420Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:28:53.046427Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:28:53.046434Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:28:53.046440Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:29:23.051828Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=30593.51 KB (evictable, flusher gauge), registry+intern≈31.65 MB (resident, not flushable), process RSS=62.4 MB
+2026-06-12T21:29:23.051912Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:29:23.051922Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:29:23.051930Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:29:23.051943Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:29:23.051951Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:29:53.046349Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=31175.93 KB (evictable, flusher gauge), registry+intern≈32.22 MB (resident, not flushable), process RSS=63.0 MB
+2026-06-12T21:29:53.046393Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:29:53.046403Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:29:53.046410Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:29:53.046417Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:29:53.046423Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:30:23.046144Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=31756.51 KB (evictable, flusher gauge), registry+intern≈32.79 MB (resident, not flushable), process RSS=63.3 MB
+2026-06-12T21:30:23.046196Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:30:23.046206Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:30:23.046213Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:30:23.046220Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:30:23.046227Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:30:53.046608Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=32341.29 KB (evictable, flusher gauge), registry+intern≈33.36 MB (resident, not flushable), process RSS=63.8 MB
+2026-06-12T21:30:53.046652Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:30:53.046662Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:30:53.046669Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:30:53.046676Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:30:53.046688Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:31:23.047219Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=32927.21 KB (evictable, flusher gauge), registry+intern≈33.93 MB (resident, not flushable), process RSS=65.3 MB
+2026-06-12T21:31:23.047508Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:31:23.047531Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:31:23.047545Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:31:23.047558Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:31:23.047577Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:31:53.046898Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=33509.48 KB (evictable, flusher gauge), registry+intern≈34.50 MB (resident, not flushable), process RSS=66.1 MB
+2026-06-12T21:31:53.046955Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:31:53.046965Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:31:53.046972Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:31:53.046979Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:31:53.046986Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:32:23.046694Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=34090.05 KB (evictable, flusher gauge), registry+intern≈35.07 MB (resident, not flushable), process RSS=66.6 MB
+2026-06-12T21:32:23.046760Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:32:23.046778Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:32:23.046792Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:32:23.046807Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:32:23.046820Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:32:53.045834Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=34674.83 KB (evictable, flusher gauge), registry+intern≈35.64 MB (resident, not flushable), process RSS=67.1 MB
+2026-06-12T21:32:53.045887Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:32:53.045897Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:32:53.045904Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:32:53.045911Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:32:53.045919Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:33:23.046137Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=35260.76 KB (evictable, flusher gauge), registry+intern≈36.21 MB (resident, not flushable), process RSS=67.5 MB
+2026-06-12T21:33:23.046182Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:33:23.046192Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:33:23.046199Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:33:23.046205Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:33:23.046212Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:33:53.046514Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=35843.19 KB (evictable, flusher gauge), registry+intern≈36.78 MB (resident, not flushable), process RSS=68.5 MB
+2026-06-12T21:33:53.046561Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:33:53.046570Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:33:53.046578Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:33:53.046585Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:33:53.046591Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:34:23.045840Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=36423.76 KB (evictable, flusher gauge), registry+intern≈37.35 MB (resident, not flushable), process RSS=69.0 MB
+2026-06-12T21:34:23.045888Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:34:23.045897Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:34:23.045904Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:34:23.045911Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:34:23.045918Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:34:53.046193Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=37008.54 KB (evictable, flusher gauge), registry+intern≈37.92 MB (resident, not flushable), process RSS=69.5 MB
+2026-06-12T21:34:53.046238Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:34:53.046247Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:34:53.046254Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:34:53.046261Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:34:53.046268Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
+2026-06-12T21:35:23.046310Z  INFO data_plane: data_plane/src/main.rs:987: [MEMORY_DIAG] SketchStore: 1004 instance(s), 1004 sid(s) with state, payload=37573.83 KB (evictable, flusher gauge), registry+intern≈38.47 MB (resident, not flushable), process RSS=70.3 MB
+2026-06-12T21:35:23.046355Z  INFO data_plane: data_plane/src/main.rs:1006: [MEMORY_DIAG] PrecomputeEngine: 4 total groups across 4 workers
+2026-06-12T21:35:23.046365Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_0: group_states_len=1
+2026-06-12T21:35:23.046372Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_1: group_states_len=2
+2026-06-12T21:35:23.046378Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_2: group_states_len=0
+2026-06-12T21:35:23.046385Z  INFO data_plane: data_plane/src/main.rs:1012: [MEMORY_DIAG]   worker_3: group_states_len=1
diff --git a/datasets_eval/soak/loadgen.py b/datasets_eval/soak/loadgen.py
new file mode 100644
index 000000000..7597b0db6
--- /dev/null
+++ b/datasets_eval/soak/loadgen.py
@@ -0,0 +1,131 @@
+#!/usr/bin/env python3
+"""loadgen.py — constant-rate OTLP/gRPC load generator for the edge soak.
+
+Reuses the OTLP/gRPC encoding from datasets_eval/google_cluster/run.py but
+drives a SUSTAINED CONSTANT rate (points/sec) by looping over the real
+Google-2019 mapped trace JSONL indefinitely, rather than pacing to the
+trace's own (very sparse) timestamps. Each loop re-bases timestamps to
+"now" so the edge keeps closing windows on fresh data and the warm store
+keeps turning over — exactly the steady-ingest condition a leak-slope
+soak needs.
+
+Usage:
+  loadgen.py --jsonl /tmp/gct-otlp.jsonl --endpoint 127.0.0.1:4317 \
+             --rate 5000 --duration 1800
+"""
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+import time
+
+import grpc
+from opentelemetry.proto.collector.metrics.v1 import metrics_service_pb2
+from opentelemetry.proto.collector.metrics.v1 import metrics_service_pb2_grpc
+from opentelemetry.proto.common.v1 import common_pb2
+
+
+def load_rows(path: str, cap: int):
+    rows = []
+    with open(path) as f:
+        for line in f:
+            line = line.strip()
+            if not line:
+                continue
+            rows.append(json.loads(line))
+            if cap and len(rows) >= cap:
+                break
+    return rows
+
+
+def build_req(rows):
+    req = metrics_service_pb2.ExportMetricsServiceRequest()
+    rm = req.resource_metrics.add()
+    a = rm.resource.attributes.add()
+    a.key = "service.name"
+    a.value.string_value = "google-cluster-soak"
+    sm = rm.scope_metrics.add()
+    per_metric = {}
+    for r in rows:
+        per_metric.setdefault(r["metric"], []).append(r)
+    now_ns = time.time_ns()
+    for mname, mrows in sorted(per_metric.items()):
+        metric = sm.metrics.add()
+        metric.name = mname
+        for r in mrows:
+            dp = metric.gauge.data_points.add()
+            dp.as_double = float(r["value"])
+            dp.time_unix_nano = now_ns
+            for k, v in sorted(r["attributes"].items()):
+                av = common_pb2.AnyValue()
+                av.string_value = str(v)
+                dp.attributes.add(key=k, value=av)
+    return req
+
+
+def main() -> int:
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--jsonl", required=True)
+    ap.add_argument("--endpoint", default="127.0.0.1:4317")
+    ap.add_argument("--rate", type=float, default=5000.0,
+                    help="target data points / second")
+    ap.add_argument("--duration", type=float, default=1800.0)
+    ap.add_argument("--batch", type=int, default=500)
+    ap.add_argument("--cap", type=int, default=200000,
+                    help="max rows loaded from trace into the loop buffer")
+    args = ap.parse_args()
+
+    rows = load_rows(args.jsonl, args.cap)
+    if not rows:
+        print("loadgen: no rows", file=sys.stderr)
+        return 2
+    print(f"loadgen: loaded {len(rows)} trace rows; target rate "
+          f"{args.rate} pts/s for {args.duration}s", file=sys.stderr)
+
+    ch = grpc.insecure_channel(
+        args.endpoint,
+        options=[("grpc.max_send_message_length", 1 << 30)])
+    stub = metrics_service_pb2_grpc.MetricsServiceStub(ch)
+
+    start = time.monotonic()
+    sent = 0
+    idx = 0
+    batch_period = args.batch / args.rate  # seconds per batch to hit rate
+    next_send = start
+    last_report = start
+    errors = 0
+    while True:
+        now = time.monotonic()
+        if now - start >= args.duration:
+            break
+        # take the next `batch` rows, wrapping
+        chunk = []
+        for _ in range(args.batch):
+            chunk.append(rows[idx])
+            idx = (idx + 1) % len(rows)
+        req = build_req(chunk)
+        try:
+            stub.Export(req, timeout=15.0)
+            sent += len(chunk)
+        except grpc.RpcError as exc:
+            errors += 1
+            if errors <= 5:
+                print(f"loadgen: export error: {exc.code()}", file=sys.stderr)
+        next_send += batch_period
+        sleep = next_send - time.monotonic()
+        if sleep > 0:
+            time.sleep(sleep)
+        if now - last_report >= 30:
+            el = now - start
+            print(f"loadgen: t={el:6.0f}s sent={sent} "
+                  f"({sent/el:.0f} pts/s) errors={errors}", file=sys.stderr)
+            last_report = now
+    el = time.monotonic() - start
+    print(f"loadgen: done. sent={sent} in {el:.0f}s "
+          f"({sent/el:.0f} pts/s) errors={errors}", file=sys.stderr)
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/datasets_eval/soak/loadgen_b0.log b/datasets_eval/soak/loadgen_b0.log
new file mode 100644
index 000000000..6271497a9
--- /dev/null
+++ b/datasets_eval/soak/loadgen_b0.log
@@ -0,0 +1,15 @@
+loadgen: loaded 200000 trace rows; target rate 5000.0 pts/s for 260.0s
+loadgen: t=    30s sent=150500 (5017 pts/s) errors=0
+loadgen: t=    60s sent=301000 (5008 pts/s) errors=0
+loadgen: t=    90s sent=451500 (5006 pts/s) errors=0
+loadgen: t=   120s sent=601500 (5004 pts/s) errors=0
+loadgen: t=   150s sent=752000 (5003 pts/s) errors=0
+loadgen: t=   180s sent=902500 (5003 pts/s) errors=0
+loadgen: t=   211s sent=1053000 (5002 pts/s) errors=0
+loadgen: export error: StatusCode.UNAVAILABLE
+loadgen: export error: StatusCode.UNAVAILABLE
+loadgen: export error: StatusCode.UNAVAILABLE
+loadgen: export error: StatusCode.UNAVAILABLE
+loadgen: export error: StatusCode.UNAVAILABLE
+loadgen: t=   241s sent=1133000 (4709 pts/s) errors=141
+loadgen: done. sent=1229500 in 260s (4729 pts/s) errors=141
diff --git a/datasets_eval/soak/loadgen_b3.log b/datasets_eval/soak/loadgen_b3.log
new file mode 100644
index 000000000..a8c42f9d3
--- /dev/null
+++ b/datasets_eval/soak/loadgen_b3.log
@@ -0,0 +1,65 @@
+loadgen: loaded 200000 trace rows; target rate 5000.0 pts/s for 1900.0s
+loadgen: t=    30s sent=150500 (5017 pts/s) errors=0
+loadgen: t=    60s sent=301000 (5008 pts/s) errors=0
+loadgen: t=    90s sent=451500 (5006 pts/s) errors=0
+loadgen: t=   120s sent=602000 (5004 pts/s) errors=0
+loadgen: t=   150s sent=752500 (5003 pts/s) errors=0
+loadgen: t=   181s sent=903000 (5003 pts/s) errors=0
+loadgen: t=   211s sent=1053000 (5002 pts/s) errors=0
+loadgen: t=   241s sent=1203500 (5002 pts/s) errors=0
+loadgen: t=   271s sent=1354000 (5002 pts/s) errors=0
+loadgen: t=   301s sent=1504000 (5002 pts/s) errors=0
+loadgen: t=   331s sent=1654000 (5002 pts/s) errors=0
+loadgen: t=   361s sent=1804500 (5001 pts/s) errors=0
+loadgen: t=   391s sent=1954500 (5001 pts/s) errors=0
+loadgen: t=   421s sent=2104500 (5001 pts/s) errors=0
+loadgen: t=   451s sent=2255000 (5001 pts/s) errors=0
+loadgen: t=   481s sent=2405500 (5001 pts/s) errors=0
+loadgen: t=   511s sent=2556000 (5001 pts/s) errors=0
+loadgen: t=   541s sent=2706500 (5001 pts/s) errors=0
+loadgen: t=   571s sent=2857000 (5001 pts/s) errors=0
+loadgen: t=   601s sent=3007500 (5001 pts/s) errors=0
+loadgen: t=   631s sent=3157500 (5001 pts/s) errors=0
+loadgen: t=   661s sent=3307500 (5001 pts/s) errors=0
+loadgen: t=   692s sent=3458000 (5001 pts/s) errors=0
+loadgen: t=   722s sent=3608000 (5001 pts/s) errors=0
+loadgen: t=   752s sent=3758500 (5001 pts/s) errors=0
+loadgen: t=   782s sent=3909000 (5001 pts/s) errors=0
+loadgen: t=   812s sent=4059000 (5001 pts/s) errors=0
+loadgen: t=   842s sent=4209000 (5001 pts/s) errors=0
+loadgen: t=   872s sent=4359500 (5001 pts/s) errors=0
+loadgen: t=   902s sent=4509500 (5001 pts/s) errors=0
+loadgen: t=   932s sent=4660000 (5001 pts/s) errors=0
+loadgen: t=   962s sent=4810500 (5001 pts/s) errors=0
+loadgen: t=   992s sent=4961000 (5001 pts/s) errors=0
+loadgen: t=  1022s sent=5111500 (5000 pts/s) errors=0
+loadgen: t=  1052s sent=5261500 (5000 pts/s) errors=0
+loadgen: t=  1082s sent=5412000 (5000 pts/s) errors=0
+loadgen: t=  1112s sent=5562500 (5000 pts/s) errors=0
+loadgen: t=  1142s sent=5712500 (5000 pts/s) errors=0
+loadgen: t=  1173s sent=5863000 (5000 pts/s) errors=0
+loadgen: t=  1203s sent=6013000 (5000 pts/s) errors=0
+loadgen: t=  1233s sent=6163500 (5000 pts/s) errors=0
+loadgen: t=  1263s sent=6313500 (5000 pts/s) errors=0
+loadgen: t=  1293s sent=6463500 (5000 pts/s) errors=0
+loadgen: t=  1323s sent=6613500 (5000 pts/s) errors=0
+loadgen: t=  1353s sent=6763500 (5000 pts/s) errors=0
+loadgen: t=  1383s sent=6913500 (5000 pts/s) errors=0
+loadgen: t=  1413s sent=7064000 (5000 pts/s) errors=0
+loadgen: t=  1443s sent=7214000 (5000 pts/s) errors=0
+loadgen: t=  1473s sent=7364500 (5000 pts/s) errors=0
+loadgen: t=  1503s sent=7515000 (5000 pts/s) errors=0
+loadgen: t=  1533s sent=7665000 (5000 pts/s) errors=0
+loadgen: t=  1563s sent=7815000 (5000 pts/s) errors=0
+loadgen: t=  1593s sent=7965500 (5000 pts/s) errors=0
+loadgen: t=  1623s sent=8115500 (5000 pts/s) errors=0
+loadgen: t=  1653s sent=8266000 (5000 pts/s) errors=0
+loadgen: t=  1683s sent=8416500 (5000 pts/s) errors=0
+loadgen: t=  1713s sent=8566500 (5000 pts/s) errors=0
+loadgen: t=  1743s sent=8717000 (5000 pts/s) errors=0
+loadgen: t=  1773s sent=8867500 (5000 pts/s) errors=0
+loadgen: t=  1803s sent=9017500 (5000 pts/s) errors=0
+loadgen: t=  1834s sent=9168000 (5000 pts/s) errors=0
+loadgen: t=  1864s sent=9318500 (5000 pts/s) errors=0
+loadgen: t=  1894s sent=9468500 (5000 pts/s) errors=0
+loadgen: done. sent=9500000 in 1900s (5000 pts/s) errors=0
diff --git a/datasets_eval/soak/rss_over_time.png b/datasets_eval/soak/rss_over_time.png
new file mode 100644
index 000000000..1bc689aff
Binary files /dev/null and b/datasets_eval/soak/rss_over_time.png differ
diff --git a/datasets_eval/soak/sampler.py b/datasets_eval/soak/sampler.py
new file mode 100644
index 000000000..09c181aad
--- /dev/null
+++ b/datasets_eval/soak/sampler.py
@@ -0,0 +1,110 @@
+#!/usr/bin/env python3
+"""sampler.py — sample edge + data_plane process RSS and CPU% from /proc.
+
+Used by the §6 edge soak (Fig 6) measurement on the real ASAP stack. Both
+the asap-otel (edge) and data_plane containers run with `--network host`,
+so their PIDs are visible on the host and /proc/<pid>/{stat,status} give
+exact RSS + CPU jiffies without any container overhead.
+
+CPU% is computed from the delta of (utime+stime) jiffies between two
+consecutive samples divided by the wall-clock delta, normalised to a
+single core (so 100% == one fully-busy core; can exceed 100% multi-core).
+
+Output: one JSON object per sample to the --out JSONL file, plus a human
+line to stderr. Run for --duration seconds at --interval seconds.
+
+Usage:
+  sampler.py --pids edge=1521738 dp=1521599 \
+             --interval 3 --duration 1800 --out samples.jsonl --tag b3
+"""
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import sys
+import time
+
+CLK_TCK = os.sysconf("SC_CLK_TCK")
+PAGE = os.sysconf("SC_PAGE_SIZE")
+
+
+def read_stat(pid: int):
+    """Return (utime+stime jiffies, rss_bytes) or None if pid is gone."""
+    try:
+        with open(f"/proc/{pid}/stat", "rb") as f:
+            data = f.read()
+        # comm may contain spaces/parens; split on last ')'
+        rparen = data.rindex(b")")
+        fields = data[rparen + 2 :].split()
+        # after comm, field index 0 == state; utime=13,stime=14 (1-based 14,15)
+        utime = int(fields[11])
+        stime = int(fields[12])
+        with open(f"/proc/{pid}/status", "r") as f:
+            rss_kb = 0
+            for line in f:
+                if line.startswith("VmRSS:"):
+                    rss_kb = int(line.split()[1])
+                    break
+        return utime + stime, rss_kb * 1024
+    except (FileNotFoundError, ProcessLookupError, ValueError):
+        return None
+
+
+def main() -> int:
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--pids", nargs="+", required=True,
+                    help="name=pid pairs, e.g. edge=123 dp=456")
+    ap.add_argument("--interval", type=float, default=3.0)
+    ap.add_argument("--duration", type=float, default=1800.0)
+    ap.add_argument("--out", required=True)
+    ap.add_argument("--tag", default="")
+    args = ap.parse_args()
+
+    pids = {}
+    for kv in args.pids:
+        name, pid = kv.split("=")
+        pids[name] = int(pid)
+
+    prev = {name: read_stat(pid) for name, pid in pids.items()}
+    prev_t = time.monotonic()
+    start = prev_t
+
+    out = open(args.out, "a", buffering=1)
+    n = 0
+    while True:
+        now = time.monotonic()
+        if now - start >= args.duration:
+            break
+        time.sleep(args.interval)
+        t = time.monotonic()
+        dt = t - prev_t
+        rec = {"t_wall": time.time(), "t_rel": round(t - start, 2),
+               "tag": args.tag}
+        for name, pid in pids.items():
+            cur = read_stat(pid)
+            if cur is None:
+                rec[name] = {"rss_mb": None, "cpu_pct": None, "alive": False}
+                continue
+            cpu_pct = None
+            if prev[name] is not None and dt > 0:
+                djiff = cur[0] - prev[name][0]
+                cpu_pct = 100.0 * (djiff / CLK_TCK) / dt
+            rec[name] = {"rss_mb": round(cur[1] / 1e6, 2),
+                         "cpu_pct": round(cpu_pct, 2) if cpu_pct is not None else None,
+                         "alive": True}
+            prev[name] = cur
+        prev_t = t
+        out.write(json.dumps(rec) + "\n")
+        n += 1
+        parts = " ".join(
+            f"{k}:rss={v['rss_mb']}MB cpu={v['cpu_pct']}%"
+            for k, v in rec.items() if isinstance(v, dict))
+        print(f"[{rec['t_rel']:7.1f}s] {parts}", file=sys.stderr)
+    out.close()
+    print(f"sampler: wrote {n} samples to {args.out}", file=sys.stderr)
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/datasets_eval/soak/samples_b0.jsonl b/datasets_eval/soak/samples_b0.jsonl
new file mode 100644
index 000000000..4a2d9bf84
--- /dev/null
+++ b/datasets_eval/soak/samples_b0.jsonl
@@ -0,0 +1,60 @@
+{"t_wall": 1781297993.0914965, "t_rel": 3.0, "tag": "b0", "edge": {"rss_mb": 202.94, "cpu_pct": 3.0, "alive": true}, "dp": {"rss_mb": 27.77, "cpu_pct": 7.66, "alive": true}}
+{"t_wall": 1781297996.0952024, "t_rel": 6.01, "tag": "b0", "edge": {"rss_mb": 203.75, "cpu_pct": 3.0, "alive": true}, "dp": {"rss_mb": 27.77, "cpu_pct": 7.66, "alive": true}}
+{"t_wall": 1781297999.0988362, "t_rel": 9.01, "tag": "b0", "edge": {"rss_mb": 204.14, "cpu_pct": 2.33, "alive": true}, "dp": {"rss_mb": 27.77, "cpu_pct": 7.66, "alive": true}}
+{"t_wall": 1781298002.1024518, "t_rel": 12.01, "tag": "b0", "edge": {"rss_mb": 205.21, "cpu_pct": 3.33, "alive": true}, "dp": {"rss_mb": 27.77, "cpu_pct": 7.66, "alive": true}}
+{"t_wall": 1781298005.1060708, "t_rel": 15.02, "tag": "b0", "edge": {"rss_mb": 205.21, "cpu_pct": 3.0, "alive": true}, "dp": {"rss_mb": 27.88, "cpu_pct": 7.66, "alive": true}}
+{"t_wall": 1781298008.109689, "t_rel": 18.02, "tag": "b0", "edge": {"rss_mb": 205.21, "cpu_pct": 2.0, "alive": true}, "dp": {"rss_mb": 28.0, "cpu_pct": 7.66, "alive": true}}
+{"t_wall": 1781298011.1123114, "t_rel": 21.02, "tag": "b0", "edge": {"rss_mb": 205.21, "cpu_pct": 3.0, "alive": true}, "dp": {"rss_mb": 28.0, "cpu_pct": 7.66, "alive": true}}
+{"t_wall": 1781298014.1159334, "t_rel": 24.03, "tag": "b0", "edge": {"rss_mb": 205.21, "cpu_pct": 2.33, "alive": true}, "dp": {"rss_mb": 28.0, "cpu_pct": 7.66, "alive": true}}
+{"t_wall": 1781298017.1195807, "t_rel": 27.03, "tag": "b0", "edge": {"rss_mb": 206.28, "cpu_pct": 3.33, "alive": true}, "dp": {"rss_mb": 28.0, "cpu_pct": 7.66, "alive": true}}
+{"t_wall": 1781298020.1203012, "t_rel": 30.03, "tag": "b0", "edge": {"rss_mb": 206.28, "cpu_pct": 3.0, "alive": true}, "dp": {"rss_mb": 28.0, "cpu_pct": 7.66, "alive": true}}
+{"t_wall": 1781298023.12396, "t_rel": 33.04, "tag": "b0", "edge": {"rss_mb": 206.28, "cpu_pct": 2.33, "alive": true}, "dp": {"rss_mb": 28.0, "cpu_pct": 7.32, "alive": true}}
+{"t_wall": 1781298026.127602, "t_rel": 36.04, "tag": "b0", "edge": {"rss_mb": 206.54, "cpu_pct": 3.0, "alive": true}, "dp": {"rss_mb": 28.02, "cpu_pct": 7.66, "alive": true}}
+{"t_wall": 1781298029.131224, "t_rel": 39.04, "tag": "b0", "edge": {"rss_mb": 206.54, "cpu_pct": 3.0, "alive": true}, "dp": {"rss_mb": 28.02, "cpu_pct": 7.66, "alive": true}}
+{"t_wall": 1781298032.1348457, "t_rel": 42.05, "tag": "b0", "edge": {"rss_mb": 206.54, "cpu_pct": 2.33, "alive": true}, "dp": {"rss_mb": 28.02, "cpu_pct": 7.66, "alive": true}}
+{"t_wall": 1781298035.1385088, "t_rel": 45.05, "tag": "b0", "edge": {"rss_mb": 206.54, "cpu_pct": 3.0, "alive": true}, "dp": {"rss_mb": 28.02, "cpu_pct": 7.66, "alive": true}}
+{"t_wall": 1781298038.1421878, "t_rel": 48.05, "tag": "b0", "edge": {"rss_mb": 205.69, "cpu_pct": 3.0, "alive": true}, "dp": {"rss_mb": 28.02, "cpu_pct": 7.99, "alive": true}}
+{"t_wall": 1781298041.1458719, "t_rel": 51.06, "tag": "b0", "edge": {"rss_mb": 205.69, "cpu_pct": 2.0, "alive": true}, "dp": {"rss_mb": 28.02, "cpu_pct": 7.32, "alive": true}}
+{"t_wall": 1781298044.1495454, "t_rel": 54.06, "tag": "b0", "edge": {"rss_mb": 205.69, "cpu_pct": 3.0, "alive": true}, "dp": {"rss_mb": 28.02, "cpu_pct": 7.66, "alive": true}}
+{"t_wall": 1781298047.1532166, "t_rel": 57.06, "tag": "b0", "edge": {"rss_mb": 205.69, "cpu_pct": 3.33, "alive": true}, "dp": {"rss_mb": 28.02, "cpu_pct": 7.66, "alive": true}}
+{"t_wall": 1781298050.1568422, "t_rel": 60.07, "tag": "b0", "edge": {"rss_mb": 205.69, "cpu_pct": 2.0, "alive": true}, "dp": {"rss_mb": 28.02, "cpu_pct": 7.66, "alive": true}}
+{"t_wall": 1781298053.1604643, "t_rel": 63.07, "tag": "b0", "edge": {"rss_mb": 205.41, "cpu_pct": 3.0, "alive": true}, "dp": {"rss_mb": 28.02, "cpu_pct": 7.66, "alive": true}}
+{"t_wall": 1781298056.1641293, "t_rel": 66.08, "tag": "b0", "edge": {"rss_mb": 205.41, "cpu_pct": 3.0, "alive": true}, "dp": {"rss_mb": 28.02, "cpu_pct": 7.66, "alive": true}}
+{"t_wall": 1781298059.1677694, "t_rel": 69.08, "tag": "b0", "edge": {"rss_mb": 205.41, "cpu_pct": 2.33, "alive": true}, "dp": {"rss_mb": 28.04, "cpu_pct": 7.99, "alive": true}}
+{"t_wall": 1781298062.1714072, "t_rel": 72.08, "tag": "b0", "edge": {"rss_mb": 205.16, "cpu_pct": 3.0, "alive": true}, "dp": {"rss_mb": 28.08, "cpu_pct": 7.66, "alive": true}}
+{"t_wall": 1781298065.17505, "t_rel": 75.09, "tag": "b0", "edge": {"rss_mb": 205.16, "cpu_pct": 2.66, "alive": true}, "dp": {"rss_mb": 28.09, "cpu_pct": 7.66, "alive": true}}
+{"t_wall": 1781298068.178671, "t_rel": 78.09, "tag": "b0", "edge": {"rss_mb": 205.16, "cpu_pct": 2.66, "alive": true}, "dp": {"rss_mb": 28.09, "cpu_pct": 7.32, "alive": true}}
+{"t_wall": 1781298071.1822894, "t_rel": 81.09, "tag": "b0", "edge": {"rss_mb": 205.16, "cpu_pct": 3.0, "alive": true}, "dp": {"rss_mb": 28.09, "cpu_pct": 7.66, "alive": true}}
+{"t_wall": 1781298074.1843038, "t_rel": 84.1, "tag": "b0", "edge": {"rss_mb": 205.16, "cpu_pct": 2.33, "alive": true}, "dp": {"rss_mb": 28.09, "cpu_pct": 7.66, "alive": true}}
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diff --git a/datasets_eval/soak/samples_b3_soak.jsonl b/datasets_eval/soak/samples_b3_soak.jsonl
new file mode 100644
index 000000000..a49fa140d
--- /dev/null
+++ b/datasets_eval/soak/samples_b3_soak.jsonl
@@ -0,0 +1,360 @@
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+{"t_wall": 1781299855.775466, "t_rel": 1616.75, "tag": "b3", "edge": {"rss_mb": 219.2, "cpu_pct": 4.0, "alive": true}, "dp": {"rss_mb": 66.86, "cpu_pct": 0.2, "alive": true}}
+{"t_wall": 1781299860.7810626, "t_rel": 1621.75, "tag": "b3", "edge": {"rss_mb": 218.47, "cpu_pct": 4.2, "alive": true}, "dp": {"rss_mb": 67.02, "cpu_pct": 0.4, "alive": true}}
+{"t_wall": 1781299865.7866688, "t_rel": 1626.76, "tag": "b3", "edge": {"rss_mb": 219.1, "cpu_pct": 4.4, "alive": true}, "dp": {"rss_mb": 67.22, "cpu_pct": 0.2, "alive": true}}
+{"t_wall": 1781299870.7883105, "t_rel": 1631.76, "tag": "b3", "edge": {"rss_mb": 219.1, "cpu_pct": 8.2, "alive": true}, "dp": {"rss_mb": 67.45, "cpu_pct": 0.6, "alive": true}}
+{"t_wall": 1781299875.7923172, "t_rel": 1636.76, "tag": "b3", "edge": {"rss_mb": 220.16, "cpu_pct": 7.99, "alive": true}, "dp": {"rss_mb": 67.57, "cpu_pct": 0.6, "alive": true}}
+{"t_wall": 1781299880.7963054, "t_rel": 1641.77, "tag": "b3", "edge": {"rss_mb": 222.49, "cpu_pct": 10.39, "alive": true}, "dp": {"rss_mb": 68.28, "cpu_pct": 0.6, "alive": true}}
+{"t_wall": 1781299885.8003097, "t_rel": 1646.77, "tag": "b3", "edge": {"rss_mb": 222.62, "cpu_pct": 8.59, "alive": true}, "dp": {"rss_mb": 68.67, "cpu_pct": 0.4, "alive": true}}
+{"t_wall": 1781299890.8043082, "t_rel": 1651.78, "tag": "b3", "edge": {"rss_mb": 222.62, "cpu_pct": 8.19, "alive": true}, "dp": {"rss_mb": 68.73, "cpu_pct": 0.4, "alive": true}}
+{"t_wall": 1781299895.8083167, "t_rel": 1656.78, "tag": "b3", "edge": {"rss_mb": 223.71, "cpu_pct": 8.39, "alive": true}, "dp": {"rss_mb": 69.21, "cpu_pct": 0.6, "alive": true}}
+{"t_wall": 1781299900.8123126, "t_rel": 1661.78, "tag": "b3", "edge": {"rss_mb": 224.47, "cpu_pct": 7.99, "alive": true}, "dp": {"rss_mb": 69.21, "cpu_pct": 0.4, "alive": true}}
+{"t_wall": 1781299905.8163192, "t_rel": 1666.79, "tag": "b3", "edge": {"rss_mb": 224.47, "cpu_pct": 7.19, "alive": true}, "dp": {"rss_mb": 69.32, "cpu_pct": 0.6, "alive": true}}
+{"t_wall": 1781299910.820304, "t_rel": 1671.79, "tag": "b3", "edge": {"rss_mb": 221.73, "cpu_pct": 7.59, "alive": true}, "dp": {"rss_mb": 69.32, "cpu_pct": 0.4, "alive": true}}
+{"t_wall": 1781299915.8243074, "t_rel": 1676.8, "tag": "b3", "edge": {"rss_mb": 222.24, "cpu_pct": 7.19, "alive": true}, "dp": {"rss_mb": 69.32, "cpu_pct": 0.4, "alive": true}}
+{"t_wall": 1781299920.8305993, "t_rel": 1681.8, "tag": "b3", "edge": {"rss_mb": 222.99, "cpu_pct": 5.59, "alive": true}, "dp": {"rss_mb": 69.35, "cpu_pct": 0.2, "alive": true}}
+{"t_wall": 1781299925.836243, "t_rel": 1686.81, "tag": "b3", "edge": {"rss_mb": 222.99, "cpu_pct": 4.99, "alive": true}, "dp": {"rss_mb": 69.55, "cpu_pct": 0.4, "alive": true}}
+{"t_wall": 1781299930.8372295, "t_rel": 1691.81, "tag": "b3", "edge": {"rss_mb": 222.99, "cpu_pct": 4.4, "alive": true}, "dp": {"rss_mb": 69.67, "cpu_pct": 0.2, "alive": true}}
+{"t_wall": 1781299935.8403018, "t_rel": 1696.81, "tag": "b3", "edge": {"rss_mb": 223.26, "cpu_pct": 4.8, "alive": true}, "dp": {"rss_mb": 69.67, "cpu_pct": 0.2, "alive": true}}
+{"t_wall": 1781299940.8443258, "t_rel": 1701.82, "tag": "b3", "edge": {"rss_mb": 223.78, "cpu_pct": 5.0, "alive": true}, "dp": {"rss_mb": 69.88, "cpu_pct": 0.2, "alive": true}}
+{"t_wall": 1781299945.8482873, "t_rel": 1706.82, "tag": "b3", "edge": {"rss_mb": 223.01, "cpu_pct": 5.4, "alive": true}, "dp": {"rss_mb": 70.12, "cpu_pct": 0.4, "alive": true}}
+{"t_wall": 1781299950.8523018, "t_rel": 1711.82, "tag": "b3", "edge": {"rss_mb": 223.45, "cpu_pct": 5.0, "alive": true}, "dp": {"rss_mb": 70.12, "cpu_pct": 0.2, "alive": true}}
+{"t_wall": 1781299955.8579662, "t_rel": 1716.83, "tag": "b3", "edge": {"rss_mb": 223.86, "cpu_pct": 6.39, "alive": true}, "dp": {"rss_mb": 70.12, "cpu_pct": 0.4, "alive": true}}
+{"t_wall": 1781299960.8636005, "t_rel": 1721.84, "tag": "b3", "edge": {"rss_mb": 223.34, "cpu_pct": 5.79, "alive": true}, "dp": {"rss_mb": 70.12, "cpu_pct": 0.4, "alive": true}}
+{"t_wall": 1781299965.868304, "t_rel": 1726.84, "tag": "b3", "edge": {"rss_mb": 223.34, "cpu_pct": 6.19, "alive": true}, "dp": {"rss_mb": 70.12, "cpu_pct": 0.4, "alive": true}}
+{"t_wall": 1781299970.8723028, "t_rel": 1731.84, "tag": "b3", "edge": {"rss_mb": 224.21, "cpu_pct": 6.0, "alive": true}, "dp": {"rss_mb": 70.37, "cpu_pct": 0.2, "alive": true}}
+{"t_wall": 1781299975.8763108, "t_rel": 1736.85, "tag": "b3", "edge": {"rss_mb": 222.08, "cpu_pct": 6.59, "alive": true}, "dp": {"rss_mb": 70.37, "cpu_pct": 0.4, "alive": true}}
+{"t_wall": 1781299980.880309, "t_rel": 1741.85, "tag": "b3", "edge": {"rss_mb": 221.07, "cpu_pct": 7.39, "alive": true}, "dp": {"rss_mb": 70.37, "cpu_pct": 0.4, "alive": true}}
+{"t_wall": 1781299985.886184, "t_rel": 1746.86, "tag": "b3", "edge": {"rss_mb": 220.01, "cpu_pct": 4.39, "alive": true}, "dp": {"rss_mb": 70.37, "cpu_pct": 0.2, "alive": true}}
+{"t_wall": 1781299990.8917782, "t_rel": 1751.86, "tag": "b3", "edge": {"rss_mb": 221.1, "cpu_pct": 4.99, "alive": true}, "dp": {"rss_mb": 70.37, "cpu_pct": 0.2, "alive": true}}
+{"t_wall": 1781299995.894805, "t_rel": 1756.87, "tag": "b3", "edge": {"rss_mb": 221.1, "cpu_pct": 4.2, "alive": true}, "dp": {"rss_mb": 70.37, "cpu_pct": 0.2, "alive": true}}
+{"t_wall": 1781300000.9004304, "t_rel": 1761.87, "tag": "b3", "edge": {"rss_mb": 221.57, "cpu_pct": 4.4, "alive": true}, "dp": {"rss_mb": 70.5, "cpu_pct": 0.2, "alive": true}}
+{"t_wall": 1781300005.9043026, "t_rel": 1766.88, "tag": "b3", "edge": {"rss_mb": 222.27, "cpu_pct": 5.0, "alive": true}, "dp": {"rss_mb": 71.02, "cpu_pct": 0.2, "alive": true}}
+{"t_wall": 1781300010.9099016, "t_rel": 1771.88, "tag": "b3", "edge": {"rss_mb": 222.27, "cpu_pct": 4.79, "alive": true}, "dp": {"rss_mb": 71.02, "cpu_pct": 0.4, "alive": true}}
+{"t_wall": 1781300015.915527, "t_rel": 1776.89, "tag": "b3", "edge": {"rss_mb": 222.27, "cpu_pct": 4.59, "alive": true}, "dp": {"rss_mb": 71.29, "cpu_pct": 0.2, "alive": true}}
+{"t_wall": 1781300020.921167, "t_rel": 1781.89, "tag": "b3", "edge": {"rss_mb": 222.78, "cpu_pct": 4.79, "alive": true}, "dp": {"rss_mb": 71.29, "cpu_pct": 0.4, "alive": true}}
+{"t_wall": 1781300025.9267986, "t_rel": 1786.9, "tag": "b3", "edge": {"rss_mb": 222.78, "cpu_pct": 5.39, "alive": true}, "dp": {"rss_mb": 71.29, "cpu_pct": 0.0, "alive": true}}
+{"t_wall": 1781300030.9283066, "t_rel": 1791.9, "tag": "b3", "edge": {"rss_mb": 223.54, "cpu_pct": 5.2, "alive": true}, "dp": {"rss_mb": 71.81, "cpu_pct": 0.4, "alive": true}}
+{"t_wall": 1781300035.934245, "t_rel": 1796.91, "tag": "b3", "edge": {"rss_mb": 223.1, "cpu_pct": 4.79, "alive": true}, "dp": {"rss_mb": 72.08, "cpu_pct": 0.2, "alive": true}}
+{"t_wall": 1781300040.9363008, "t_rel": 1801.91, "tag": "b3", "edge": {"rss_mb": 223.67, "cpu_pct": 5.0, "alive": true}, "dp": {"rss_mb": 72.08, "cpu_pct": 0.4, "alive": true}}
diff --git a/datasets_eval/soak/soak_RESULTS.md b/datasets_eval/soak/soak_RESULTS.md
new file mode 100644
index 000000000..2dad07a73
--- /dev/null
+++ b/datasets_eval/soak/soak_RESULTS.md
@@ -0,0 +1,148 @@
+# §6 Edge soak — CPU/RSS bounded-ness + leak slope (Fig 6)
+
+Measured on the **real ASAP stack** (cold-OFF single-host), feeding the real
+Google-2019 cluster trace (`/tmp/gct-otlp.jsonl`, the mapper output of
+`datasets_eval/google_cluster/run.py map --year 2019`, 200 000 rows, metric
+`google_cluster_2019_cpu_rate` + `_memory_usage`) at a **fixed 5000 pts/s**
+through `otel-app/loadgen → asap-otel edge → data_plane`.
+
+Branch: `feat/edge-soak`. Main untouched. No git worktree was used (worked
+in-place on the existing built checkout so the patched OTel-contrib build /
+docker images did not have to be rebuilt).
+
+## Setup (honest scope)
+
+- **Single-node loopback.** All three containers (`asap-data-plane`,
+  `asap-control-plane`, `asap-agent-a`) run `--network host` on node0 via
+  `datasets_eval/soak/stack-coldoff.sh` (copied from `feat/multisketch-accuracy`).
+  No network hop, no multi-node fan-in.
+- **Cold tier OFF** (no MinIO/Thanos/gorilla-merger; data_plane registers the
+  NoDataArchiveEngine stub). Warm `SketchStore` is the only live engine, so
+  everything we measure is the warm path.
+- **Ingest rate:** 5000 data points/sec, constant, generated by
+  `loadgen.py` looping the 200k-row trace and re-basing every batch's
+  timestamps to "now" (so windows keep closing on fresh data). b3 soak
+  delivered **9,500,000 points over 1900 s at 5000 pts/s, 0 gRPC errors**.
+- **Soak duration: 30 min (1800 s) of steady ingest.** A 24 h soak is OUT OF
+  SCOPE; the 24 h numbers below are **linear extrapolations** of the measured
+  30-min slope and are labelled as such.
+- **Sampling:** `sampler.py` reads `/proc/<pid>/{stat,status}` for the edge
+  (`asap-otel`) and `data_plane` host PIDs. b0 arm @ 3 s × 180 s (60 samples);
+  b3 soak @ 5 s × 1800 s (360 samples). data_plane *also* self-reports RSS +
+  `SketchStore` sid count every 30 s via its `[MEMORY_DIAG]` log line (65
+  lines captured) — used as an independent cross-check of the /proc RSS.
+
+### Arms
+
+- **sketch (b3):** the normal fused edge — `asap_edge` with
+  `google_cluster_2019_cpu_rate → DDSketch` (per-series quantile, α=0.01,
+  warm) + `google_cluster_2019_memory_usage → Sum aggregate_by [zone]`
+  (warm). Config: `asap-otel-agent-gct-b3.yaml`. **This is the
+  DDSketch + Sum warm path.**
+- **raw-forward (b0):** MEASURED. A plain collector pipeline
+  `otlp → memory_limiter → batch → otlp/backend` with **no `asap_edge`**,
+  same image, same receiver, same backend endpoint (`data-plane:14317`),
+  same 5000 pts/s. Config: `asap-otel-agent-gct-b0.yaml`. Isolates the edge
+  cost of sketch aggregation vs pure passthrough.
+
+## (a) Edge & data_plane CPU / RSS
+
+CPU% is normalised to one core (100% = one busy core).
+
+| arm | mean CPU% | p99 CPU% | steady RSS | n |
+|---|---|---|---|---|
+| **b0 raw-forward edge** (asap-otel) | 2.75 | 3.33 | **207 MB** | 60 |
+| **b3 sketch edge** (asap-otel), first 180 s | 4.24 | 5.19 | **223 MB** | 35 |
+| b3 sketch edge, full 30-min soak | 4.38 | 8.19 | 223 MB | 360 |
+| b0 data_plane | 7.69 | 8.13 | 28 MB | 60 |
+| b3 data_plane, first 180 s | 0.24 | 0.53 | 29 MB | 35 |
+| b3 data_plane, full 30-min soak | 0.26 | 0.60 | 25→72 MB (climbing) | 360 |
+
+Edge takeaways:
+- The **sketch edge costs ~1.6× the CPU** of raw-forward (mean 4.4% vs 2.8%
+  of one core) and **~16 MB more RSS** (223 vs 207 MB) at 5000 pts/s. Both
+  are tiny in absolute terms — the edge is comfortably bounded on one node.
+- The b3 edge p99 (8.2%) > raw-forward p99 (3.3%): the per-window DDSketch
+  seal/serialize/ship work shows up as brief CPU spikes on the 30 s flush
+  tick, but RSS does not move with them.
+- data_plane CPU is higher in b0 (7.7%) than b3 (0.26%): in b0 the backend
+  ingests the **full raw 1004-series stream**, whereas in b3 it ingests a few
+  compact sketch/Sum envelopes per window — the aggregation gain is real and
+  visible at the backend CPU.
+
+## (b) Leak slope (RSS over time, fitted over the tail half of the soak)
+
+| process | source | slope (MB/h) | verdict | 24 h extrapolation* |
+|---|---|---|---|---|
+| **edge** (asap-otel, b3) | /proc | **+2.6** | **BOUNDED** (≈0, plateaus at ~223 MB) | +63 MB |
+| **data_plane** (b3) | /proc | **+89.9** | **CLIMBING (monotone)** | +2.16 GB |
+| **data_plane** (b3) | self-report MEMORY_DIAG | **+85.2** | **CLIMBING (monotone)** | +2.05 GB |
+
+\* 24 h figures are **linear extrapolations of the measured 30-min slope**,
+not measured. They assume the per-sid growth below does not saturate, which it
+may once each sketch's bins fill — treat as a worst-case lower bound on a
+real leak, not a guarantee of +2 GB/day.
+
+- **Edge: BOUNDED.** RSS sits at ~218–224 MB for the whole 30 min with no
+  trend (see `rss_over_time.png`, top panel, blue). The memory_limiter +
+  per-shard window flush keep the edge flat under sustained load.
+- **data_plane: CLIMBING, monotone, linear.** RSS rose 25 MB → 72 MB over
+  30 min, ~+89 MB/h, no sign of a plateau within the window. The two
+  independent RSS sources (/proc and the binary's own MEMORY_DIAG gauge)
+  agree within ~5% (89.9 vs 85.2 MB/h), so this is a real backend climb, not
+  a measurement artefact.
+
+## Prior finding ([[gct-memory-findings]], 2026-05-28): reproduce or refute?
+
+Prior claim: *data_plane retains stale sketch sids (~600 MiB flat at idle,
+didn't release) and `approx_memory_bytes` under-reports ~10000× so
+back-pressure is blind.*
+
+**Partially reproduces — with a refined mechanism:**
+
+- **Stale-sid COUNT does NOT grow.** `SketchStore` sids plateau hard at
+  **1004** (the mapper's cardinality cap) within the first ~90 s and stay
+  flat for the entire soak (bottom panel, dotted red line). So the
+  "sid-count keeps climbing" reading of the prior finding does **not**
+  reproduce here — the per-shard `max_series` cap and the fixed-cardinality
+  trace bound the sid set.
+- **But per-sid STATE grows unbounded, and that is what climbs the RSS.**
+  The MEMORY_DIAG `payload` for those same 1004 sids rose
+  **257 KB → 37,574 KB (≈146×)** over 30 min — i.e. each DDSketch's bin set
+  keeps accreting as new values land in the same sids window after window,
+  and the warm store never trims it back. data_plane RSS tracks this payload
+  growth almost 1:1. So the **backend memory climb the prior note flagged is
+  real and reproduces**; the driver in this cold-OFF/capped-cardinality run
+  is *per-sid sketch-state growth*, not *sid-count growth*.
+- **The retention is BACKEND-SIDE, not edge-side.** The edge RSS is flat; only
+  data_plane climbs. Confirmed by sampling both processes.
+- **`approx_memory_bytes` under-reporting:** MEMORY_DIAG still labels the 37 MB
+  of `payload` as "evictable (flusher gauge)" while it is plainly resident and
+  not being evicted under steady load — consistent with the prior note that
+  the back-pressure accounting under-reports and would not trigger. Not
+  separately quantified here (no idle-release phase was run), so this part is
+  *consistent-with* rather than *re-measured*.
+
+## Bottom line
+
+- **Edge is bounded** in both CPU and RSS; sketch (b3) costs ~1.6× CPU and
+  +16 MB RSS over raw-forward (b0) — cheap, flat, no leak (+2.6 MB/h ≈ 0).
+- **data_plane is the memory risk:** monotone ~+87 MB/h climb under steady
+  load. NOT from unbounded sid count (that's capped at 1004) but from
+  unbounded per-sid warm-sketch state, which the flusher gauge does not
+  reclaim under continuous ingest. Extrapolated ~+2 GB/24h (linear, worst
+  case). This is backend-side, matching and refining the 2026-05-28 finding.
+
+## Files
+
+- `stack-coldoff.sh` — stack bring-up (from `feat/multisketch-accuracy`)
+- `asap-otel-agent-gct-b3.yaml` — sketch edge config (DDSketch + Sum)
+- `asap-otel-agent-gct-b0.yaml` — raw-forward edge config
+- `loadgen.py` — constant-rate OTLP/gRPC trace loop
+- `sampler.py` — /proc RSS+CPU sampler
+- `analyze.py` — stats + slope fit + plot
+- `samples_b0.jsonl`, `samples_b3_soak.jsonl` — raw /proc samples
+- `dp_memdiag_b3.log` — data_plane self-reported RSS + sid count (30 s cadence)
+- `loadgen_b0.log`, `loadgen_b3.log` — load generator logs
+- `summary.json` — machine-readable results
+- `rss_over_time.png` — Fig 6
diff --git a/datasets_eval/soak/stack-coldoff.sh b/datasets_eval/soak/stack-coldoff.sh
new file mode 100755
index 000000000..116772f04
--- /dev/null
+++ b/datasets_eval/soak/stack-coldoff.sh
@@ -0,0 +1,105 @@
+#!/usr/bin/env bash
+# stack-coldoff.sh — MINIMAL cold/archive-OFF single-host ASAP stack for the
+# multi-sketch family ACCURACY eval (feat/multisketch-accuracy).
+#
+# Difference vs stack.sh: NO MinIO / Thanos / gorilla-merger cold tier, and
+# the data-plane is started WITHOUT `ASAP_THANOS_QUERY_URL` (so the binary
+# registers the NoDataArchiveEngine stub — there is no real archive engine).
+# The agent runs with `cold: {enabled: false}` (no gorillas3 ship). This is
+# the warm-only path the Pareto/gct DDSketch accuracy runs used: recent
+# range-selector queries (quantile_over_time(...[Ns]), count_over_time(...))
+# stay on the warm SketchStore instead of being forwarded to the (empty)
+# archive.
+#
+# Components (all --network host on node0):
+#   data-plane OTLP ingest :14317/14318, query :9091  (cold OFF, no thanos)
+#   control-plane HTTP/OpAMP/gRPC :8080/4320/4321 (POSTs backend streaming-config)
+#   agent (bare fused asap_edge) OTLP receiver :4317/4318 (replay target),
+#     metrics :8890
+#
+# Usage:
+#   stack-coldoff.sh up   <workload.yaml> <agent-config.yaml>
+#   stack-coldoff.sh down
+#   stack-coldoff.sh ps
+set -uo pipefail
+
+ROOT=/mydata/ASAPCollector
+CFG=${ROOT}/deploy/mvp-multinode/configs
+WORKDIR=/mydata/mvp-multinode
+HERE=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)
+
+H=127.0.0.1
+ADD_HOSTS=(
+  --add-host=control-plane:${H} --add-host=data-plane:${H}
+  --add-host=agent-a:${H}
+)
+
+DR() { docker run -d --restart no --network host "${ADD_HOSTS[@]}" "$@"; }
+log(){ printf '[stack-coldoff %s] %s\n' "$(date +%H:%M:%S)" "$*" >&2; }
+
+down() {
+  docker ps -a --format '{{.Names}}' | grep '^asap-' | xargs -r docker rm -f >/dev/null 2>&1
+  log "all asap-* containers removed"
+}
+
+wipe_state() {
+  rm -rf "${WORKDIR}"/data/sketch-persistence/* 2>/dev/null \
+    || sudo rm -rf "${WORKDIR}"/data/sketch-persistence/* 2>/dev/null || true
+  mkdir -p "${WORKDIR}"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/data/sketch-persistence "${WORKDIR}"/configs
+}
+
+up() {
+  local workload=$1 agentcfg=$2
+  down; wipe_state
+
+  mkdir -p "${WORKDIR}"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/configs/asap "${WORKDIR}"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/configs/shared
+  cp "${CFG}/asap/backend-streaming.yaml" "${WORKDIR}/configs/asap/"
+  cp "${CFG}/asap/backend-storage-routing.yaml" "${WORKDIR}/configs/asap/"
+  cp "${workload}" "${WORKDIR}/configs/asap/eval-workload.yaml"
+  cp "${agentcfg}" "${WORKDIR}/configs/asap/eval-agent.yaml"
+
+  log "data-plane up (cold OFF — NO ASAP_THANOS_QUERY_URL; OTLP :14317, query :9091)"
+  # NOTE: no ASAP_THANOS_QUERY_URL, no ASAP_GORILLA_S3_* → the binary
+  # registers the NoDataArchiveEngine stub; warm SketchStore is the only
+  # live engine. Persistence disabled (in-memory warm sketches stay resident
+  # and queryable for the whole eval window).
+  DR --name asap-data-plane --cpus=8 \
+     -e RUST_LOG="${DP_RUST_LOG:-info}" -e ASAP_SKETCH_FAMILY=ddsketch \
+     -e ASAP_BACKEND_STORAGE_ROUTING=/etc/asap/backend-storage-routing.yaml \
+     -v "${WORKDIR}/configs/asap/backend-streaming.yaml:/etc/asap/streaming.yaml:ro" \
+     -v "${WORKDIR}/configs/asap/backend-storage-routing.yaml:/etc/asap/backend-storage-routing.yaml:ro" \
+     -v "${WORKDIR}/data/sketch-persistence:/data/sketch-persistence" \
+     asap/data-plane:dev \
+     --streaming-config=/etc/asap/streaming.yaml --query-port=9091 \
+     --enable-otel-ingest --otel-grpc-port=14317 --otel-http-port=14318 >/dev/null
+  sleep 4
+
+  log "control-plane up (workload=$(basename "${workload}"), backend OTLP port 14317)"
+  DR --name asap-control-plane --cpus=2 \
+     -e RUST_LOG="info,controller=debug,control_plane=debug" \
+     -e USE_TYPED_STAGE_SPLIT=1 -e ASAP_EDGE_FUSED=1 \
+     -e ASAP_EDGE_BACKEND_OTLP_PORT=14317 \
+     -e CONTROLLER_ADDR=0.0.0.0:8080 -e CONTROLLER_OPAMP_ADDR=0.0.0.0:4320 \
+     -e CONTROLLER_GRPC_ADDR=0.0.0.0:4321 \
+     -e CONTROLLER_OPAMP_ENDPOINT=ws://control-plane:4320/v1/opamp \
+     -e CONTROLLER_BACKEND_ENDPOINT=http://data-plane:9091/api/v1/streaming-config \
+     -e CONTROLLER_WORKLOADS=/etc/asap/eval-workload.yaml \
+     -v "${WORKDIR}/configs/asap/eval-workload.yaml:/etc/asap/eval-workload.yaml:ro" \
+     asap/control-plane:dev >/dev/null
+  sleep 5
+
+  log "agent (bare, static fused asap_edge, cold OFF) up — OTLP receiver :4317 for replay"
+  DR --name asap-agent-a --cpus=8 --hostname agent-a \
+     -e AGENT_ID=agent-a \
+     -v "${WORKDIR}/configs/asap/eval-agent.yaml:/etc/otel/config.yaml:ro" \
+     asap/asap-otel:dev --config=/etc/otel/config.yaml >/dev/null
+  sleep 6
+  log "stack up. containers:"; docker ps --format '  {{.Names}}\t{{.Status}}' | grep asap- >&2
+}
+
+case "${1:-}" in
+  up)   up "${2:?need workload}" "${3:?need agentcfg}" ;;
+  down) down ;;
+  ps)   docker ps --format '{{.Names}}\t{{.Status}}' | grep asap- ;;
+  *) echo "usage: stack-coldoff.sh up <workload> <agentcfg> | down | ps" >&2; exit 2 ;;
+esac
diff --git a/datasets_eval/soak/summary.json b/datasets_eval/soak/summary.json
new file mode 100644
index 000000000..cae4c7834
--- /dev/null
+++ b/datasets_eval/soak/summary.json
@@ -0,0 +1,72 @@
+{
+  "arms": {
+    "b0_raw_forward_edge": {
+      "n": 60,
+      "mean_cpu_pct": 2.75,
+      "p99_cpu_pct": 3.33,
+      "steady_rss_mb": 206.8,
+      "rss_min_mb": 202.9,
+      "rss_max_mb": 207.4
+    },
+    "b0_data_plane": {
+      "n": 60,
+      "mean_cpu_pct": 7.69,
+      "p99_cpu_pct": 8.13,
+      "steady_rss_mb": 28.1,
+      "rss_min_mb": 27.8,
+      "rss_max_mb": 28.1
+    },
+    "b3_sketch_edge_first180s": {
+      "n": 35,
+      "mean_cpu_pct": 4.24,
+      "p99_cpu_pct": 5.19,
+      "steady_rss_mb": 223.1,
+      "rss_min_mb": 218.7,
+      "rss_max_mb": 223.8
+    },
+    "b3_data_plane_first180s": {
+      "n": 35,
+      "mean_cpu_pct": 0.24,
+      "p99_cpu_pct": 0.53,
+      "steady_rss_mb": 28.7,
+      "rss_min_mb": 25.5,
+      "rss_max_mb": 29.8
+    },
+    "b3_sketch_edge_fullsoak": {
+      "n": 360,
+      "mean_cpu_pct": 4.38,
+      "p99_cpu_pct": 8.19,
+      "steady_rss_mb": 222.7,
+      "rss_min_mb": 218.5,
+      "rss_max_mb": 224.5
+    },
+    "b3_data_plane_fullsoak": {
+      "n": 360,
+      "mean_cpu_pct": 0.26,
+      "p99_cpu_pct": 0.6,
+      "steady_rss_mb": 60.9,
+      "rss_min_mb": 25.5,
+      "rss_max_mb": 72.1
+    }
+  },
+  "soak": {
+    "duration_s": 1801.9,
+    "duration_h": 0.499,
+    "edge_rss_slope_mb_per_h": 2.63,
+    "edge_verdict": "BOUNDED (slope +2.63 MB/h ~= 0; 24h extrap +63.1 MB)",
+    "dp_rss_slope_mb_per_h": 89.9,
+    "dp_verdict": "CLIMBING (slope +89.90 MB/h; 24h extrap +2157.6 MB)"
+  },
+  "data_plane_memdiag": {
+    "n": 65,
+    "sids_min": 512,
+    "sids_max": 1004,
+    "sids_final": 1004,
+    "payload_kb_final": 37573.83,
+    "rss_mb_min": 17.8,
+    "rss_mb_max": 70.3,
+    "rss_mb_final": 70.3,
+    "rss_slope_mb_per_h": 85.21,
+    "rss_verdict": "CLIMBING (slope +85.21 MB/h; 24h extrap +2045.0 MB)"
+  }
+}
\ No newline at end of file
diff --git a/deploy/docker/Dockerfile.otel-app b/deploy/docker/Dockerfile.otel-app
index faa4356df..9a0934d4a 100644
--- a/deploy/docker/Dockerfile.otel-app
+++ b/deploy/docker/Dockerfile.otel-app
@@ -47,10 +47,24 @@ COPY opentelemetry-go     /src/opentelemetry-go
 COPY opentelemetry-proto  /src/opentelemetry-proto
 COPY otel-app             /src/otel-app
 
+# otel-app/sample_controller.go imports asap-precompute-go/monitor{,/grpcclient}
+# (the coordinated-sampling client), and otel-app/go.mod replaces them at
+# ../asap-precompute-go{,/monitor/grpcclient}. That sibling tree lives outside
+# this Dockerfile's default context, so supply it as a named build context and
+# COPY it to /src/asap-precompute-go (the `../` replace then resolves verbatim).
+#   docker build --build-context asap-precompute-go=/path/to/asap-precompute-go ...
+COPY --from=asap-precompute-go . /src/asap-precompute-go
+
 # Sketchlib-go pulled via named build context to avoid a submodule
 # hop outside this repo.
 COPY --from=sketchlib-go . /src/sketchlib-go
 
+# asap-precompute-go (monitor.Engine + grpcclient, used by the coordinated
+# sampling edge) pulled via named build context. go.mod replaces it with
+# ../asap-precompute-go; from /src/otel-app that resolves to /src/asap-precompute-go,
+# so copy it there — no replace rewrite needed.
+COPY --from=asap-precompute-go . /src/asap-precompute-go
+
 # The go.mod replace references ../../sketchlib-go (the local checkout sits at
 # the workspace root, one level above the repo root). Inside /src it was
 # COPY'd to /src/sketchlib-go, so rewrite the replace to that absolute path.
diff --git a/deploy/mvp-multinode/configs/asap/gos-aniso-agent.yaml.tmpl b/deploy/mvp-multinode/configs/asap/gos-aniso-agent.yaml.tmpl
new file mode 100644
index 000000000..c17572799
--- /dev/null
+++ b/deploy/mvp-multinode/configs/asap/gos-aniso-agent.yaml.tmpl
@@ -0,0 +1,75 @@
+# GOS per-cell threshold eval — minimal agent config template.
+# Rendered by scripts/gos_aniso_cluster.sh: __ANISO__ → true|false,
+# __WARM_IP__ → the sink node's IP, __EDGE_ID__ → arm label.
+#
+# One CountSketch metric (top_endpoint_qps, Zipf endpoint label from the
+# otel-app five-sketch workload), delta transmission gated by the GOS
+# norm-adaptive threshold: isotropic scalar vs anisotropic per-cell {T_j}
+# (the arm under test). The filter keeps every other metric off the wire so
+# the node2 port-4317 byte counter measures ONLY this metric's delta frames.
+receivers:
+  otlp:
+    protocols:
+      grpc:
+        endpoint: 0.0.0.0:4317
+        max_recv_msg_size_mib: 4096
+
+processors:
+  memory_limiter:
+    check_interval: 1s
+    limit_mib: 1280
+    spike_limit_mib: 256
+
+  # Keep ONLY the metric under test: unconfigured metrics would otherwise
+  # passthrough raw (drop_original forwards unmatched metrics) and pollute the
+  # byte measurement.
+  filter/only_countsketch:
+    metrics:
+      include:
+        match_type: strict
+        metric_names:
+          - top_endpoint_qps
+
+  cumulativetodelta:
+    include:
+      metrics:
+        - top_endpoint_qps
+      match_type: strict
+
+  asap_edge:
+    shard_count: 4
+    window_duration: 15s
+    edge_id: __EDGE_ID__
+    drop_original: true
+    metrics:
+      - metric: top_endpoint_qps
+        family: countsketch
+        tier: warm
+        rows: 5
+        cols: 2048
+        delta_transmission: true
+        item_label: endpoint
+        # GOS norm-adaptive relative delta gate (the knobs under test). Note:
+        # emit_heap must stay OFF — the anisotropic per-cell path is gated on
+        # the non-heap wire format.
+        gos_delta_epsilon: 0.1
+        gos_sites: 4
+        gos_anisotropic: __ANISO__
+    cold:
+      enabled: false
+
+exporters:
+  otlp/backend:
+    endpoint: __WARM_IP__:4317
+    tls:
+      insecure: true
+
+service:
+  pipelines:
+    metrics:
+      receivers: [otlp]
+      processors: [memory_limiter, filter/only_countsketch, cumulativetodelta, asap_edge]
+      exporters: [otlp/backend]
+  telemetry:
+    logs:
+      level: info
diff --git a/deploy/mvp-multinode/configs/asap/gos-eval-sink.yaml b/deploy/mvp-multinode/configs/asap/gos-eval-sink.yaml
new file mode 100644
index 000000000..37f8102df
--- /dev/null
+++ b/deploy/mvp-multinode/configs/asap/gos-eval-sink.yaml
@@ -0,0 +1,22 @@
+# GOS per-cell eval — OTLP sink for the WARM node: accept + ACK everything,
+# store nothing. The byte measurement happens in the kernel (iptables dport
+# 4317 counter); the sink only needs to keep the gRPC stream healthy so the
+# agent's exporter never backs off/retries (which would distort bytes).
+receivers:
+  otlp:
+    protocols:
+      grpc:
+        endpoint: 0.0.0.0:4317
+        max_recv_msg_size_mib: 4096
+
+exporters:
+  nop:
+
+service:
+  pipelines:
+    metrics:
+      receivers: [otlp]
+      exporters: [nop]
+  telemetry:
+    logs:
+      level: warn
diff --git a/deploy/mvp-multinode/configs/asap/mvp-workload-fig9-f2.yaml b/deploy/mvp-multinode/configs/asap/mvp-workload-fig9-f2.yaml
new file mode 100644
index 000000000..757ebfec1
--- /dev/null
+++ b/deploy/mvp-multinode/configs/asap/mvp-workload-fig9-f2.yaml
@@ -0,0 +1,242 @@
+# MVP demo controller input — three canonical query classes
+#
+# Read by the controller at startup (env: CONTROLLER_WORKLOADS,
+# default ./workloads.yaml). The controller's L1 query_language →
+# L5 stage_split pipeline plans sketches + stage placement based
+# on this workload.
+#
+# Schema mirrors deploy/mvp-singlenode/configs/workloads.yaml. See
+# controller/src/config/workloads.rs::WorkloadEntry.
+#
+# Each entry is one PromQL query class the demo will exercise.
+#
+# ── Operating-point assumptions for the bandwidth verdict ─────────
+#
+# Agents run at 10 Hz scrape (`-freq-hz=10`, see
+# `deploy/mvp-singlenode/docker-compose/base.yml` and `mvp-multi-stage.yml`); each
+# series therefore produces 600 samples per 60 s flush window. The
+# break-even against raw scrape depends on `samples_per_window`
+# (≈ `state_size_bytes / per_sample_raw_bytes`); at 600 samples the
+# four delta-capable families (DDSketch / HLL / CountSketch /
+# Count-Min) sit above or close to their break-even curves.
+#
+# The controller's plan emitter
+# (`controller/src/config/stage_config.rs::build_edge_processor_block`)
+# accordingly sets `delta_transmission: true` for those four families
+# in the wire YAML it pushes to each agent. KLL is the only family
+# without a delta variant (`Implementation.tex`: "KLL has no delta
+# variant and matches its full cost"), so its wire payload is always
+# full state — the controller does NOT emit `delta_transmission` for
+# KLL because the kllprocessor's `Config.Validate` rejects
+# `delta_transmission: true` outright.
+#
+# See `docs/mvp-demo-runbook.md` §"Bandwidth criterion ① — break-even
+# depends on samples_per_window" for the per-family table.
+
+# 1. WINDOW AGGREGATION PER SERIES — DDSketch family
+#    Inner: per-series quantile over a 30s sliding window.
+#    Stage: edge (per-agent sketch flushes at window close;
+#    backend only stores resulting sketch state).
+#
+#    Window-alignment note (issue #46 ε-bound bug, fix/quantile-
+#    window-alignment): the warm-tier ASAPQuery streaming pre-compute
+#    is configured with `windowSize: 30` (see
+#    `deploy/mvp-singlenode/configs/backend-streaming.yaml` and the `precompute_window
+#    [..) ms (width 30000 ms)` annotation the warm response stamps).
+#    The replay range MUST match that pre-compute width so the warm
+#    answer is computed over the same data the replay asks for; any
+#    mismatch (e.g. `[1m]` vs warm's 30s) produces rel-err well above
+#    the DDSketch ε bound (observed mean 0.126 with `[1m]`, ε=0.01).
+- metric_name: http_requests_total_latency_ms
+  query_string: "quantile_over_time(0.99, http_requests_total_latency_ms[30s])"
+  accuracy_sla: 0.01
+  assign_to_role: agent
+  sketch_family_override: KLL   # KLL accuracy experiment (issue: rank-error vs DDSketch's value-error)
+  # NO grouping_labels — per-series KLL preserves PromQL semantics.
+  # `quantile_over_time` is per-series in PromQL; grouping the sketch at
+  # [zone] would merge all series in a zone into one sketch, returning
+  # zone-merged p99 (not per-series p99). That's a different quantity than
+  # baseline (b0/b1) computes from raw values. For accuracy comparison
+  # to be apples-to-apples, asap MUST keep per-series sketches here. The
+  # cost is ~25× more sketch state vs per-zone merge; the demo accepts
+  # that on this metric to enable correct quantile-accuracy validation.
+  # Outer aggregation operators (e.g. `max by (zone)`) then fold per-series
+  # rows correctly via the engine's apply_outer_agg_fold (PR #297).
+
+# 2. LABEL AGGREGATION ACROSS SERIES AT ONE TIMESTAMP
+#    Outer: sum-by(zone) at instant. Inner: identity.
+#    Stage: agent (per-agent Sum-by-zone is emitted under
+#    a stable sid; backend's `evaluate_exact_agg` reducer
+#    pulls sids from every agent at query time and merges
+#    across hosts — see ASAPQuery-backend PR #283/#287/#290).
+#
+#    Pre-#400 this used `assign_to_role: gateway`, which
+#    spawned an asap-gateway OTel-collector hop solely to
+#    do that cross-agent merge. Now that the backend does
+#    the merge natively, the gateway hop is redundant; the
+#    role flips to `agent` so the controller's typed
+#    stage_split emits only Edge + Backend stages (no
+#    Gateway stage → no gateway YAML emit → no asap-gateway
+#    container required).
+- metric_name: http_requests_total
+  query_string: "sum by (zone) (http_requests_total)"
+  accuracy_sla: 0.0
+  assign_to_role: agent
+  grouping_labels:
+  - zone
+  # Fig 9 (whole-sketch / F2 variant) — http_requests_total as a monitored
+  # standing query whose functional is the WHOLE-SKETCH L2 mass F2 = ‖f‖₂²
+  # (NOT a single point). This is the right model when the queried point is not
+  # known a priori: each edge reports its local F2_i = Σ_x f_i(x)² and gets
+  # p_i ∝ √(F2_i/rate_i), which keeps the entire sketch within ε so ANY future
+  # point query is accurate (see ASAPQuery-backend#380 decision rule).
+  #   Differentiation here is by DISTRIBUTION SKEW, not rate: the trace gives each
+  #   edge equal rate but different concentration (F2/rate = c), so p_i ∝ √c.
+  # COORDINATION params (not placeholders): the coordinator picks this monitor up
+  # live from the controller push (hot-reload, ASAPQuery-backend#379) and uses them
+  # directly. No per-point key — whole-sketch identity is (agg_id, "").
+  monitor:
+    tau: 30000000.0      # Σ≈1.7e7 (equal F2 on all 3 edges). τ keeps the shared
+                         #   slack Δ/(2k)≈2.1e6 below each edge's per-window F2 so
+                         #   ALL edges report, while 0.8τ=2.4e7 > Σ (no false alert)
+    functional: f2       # whole-sketch L2; edge reports F2_i = Σ_x f_i(x)²
+    epsilon: 0.2         # alert fires at (1−ε)τ
+    window_secs: 15      # MUST match the edge SDK window (15 s)
+
+# 3. COMBINED WINDOW + LABEL AGGREGATION
+#    Inner: rate per series over 5m. Outer: sum-by(zone).
+#    Stage: edge does rate (small per-series sliding window),
+#    gateway does sum-by-zone fan-in.
+- metric_name: http_requests_total
+  query_string: "sum by (zone) (rate(http_requests_total[5m]))"
+  accuracy_sla: 0.01
+  assign_to_role: agent
+  grouping_labels:
+  - zone
+
+# 4. AD-HOC COLD-FALLBACK PROBE — exercises Gorilla archive tier
+#    Metric is configured StorageBackend::GorillaS3 in
+#    backend-storage-routing.yaml so the warm tier doesn't cover
+#    it; the query forces routing to GorillaQueryEngine. Drives
+#    criterion ⑤ verification (data_source: gorilla_archive).
+#
+#    Predicate uses `zone="z0"` because the otel-app emits
+#    `zone, rack, node, pod` labels (see `otel-app/main.go`
+#    `attrSetsZRNP`); there is NO `service` label on the produced
+#    series, so the previous `service="payments"` selector matched
+#    zero series and surfaced as an empty (but HTTP-200) cold-tier
+#    response. `zone="z0"` matches roughly 1/4 of the produced
+#    series and exercises the same routing+engine code path.
+- metric_name: http_requests_total
+  query_string: "count(http_requests_total{zone=\"z0\"})"
+  accuracy_sla: 0.0
+  assign_to_role: archive
+  grouping_labels:
+  - zone
+
+# ── Five-sketch MVP coverage (issue #46) ──────────────────────────
+#
+# The four entries below register the new metrics emitted by the
+# otel-app's five-sketch workload (otel-app/
+# five_sketch_workload.go). Each entry pins a `sketch_family_override`
+# hint so the controller's planner picks the family the empirical
+# claim is scoped to (DDSketch claims rel-err on quantiles, KLL
+# claims rank-err, HLL claims cardinality, CountSketch claims
+# top-K, CountMinSketch claims one-sided frequency over-estimate).
+#
+# `target_path` records the storage tier the planner should
+# preferentially place each sketch in. Today's WorkloadEntry struct
+# (controller/src/config/workloads.rs) doesn't yet read these two
+# keys — `serde` ignores unknown fields by default, so they round-
+# trip silently until the parallel capability_matching + planner
+# work picks them up. The keys are spelled the same as the
+# capability_matching agent's expected schema so there's no rename
+# step at integration.
+
+# 5. KLL — rank-error quantile on a heavy-tailed body-size dist.
+#    Inner: per-series quantile over a 30s sliding window.
+#    Stage: edge — KLL state flushes per window close, no delta
+#    variant (kllprocessor's Config.Validate rejects
+#    delta_transmission: true). Backend stores the resulting KLL
+#    state.
+#
+#    See entry 1 above for the window-alignment rationale: the warm
+#    tier pre-computes 30s tumbling windows, so the replay range
+#    MUST be `[30s]` for warm answers to be valid against the
+#    archive ground truth.
+- metric_name: request_size_bytes
+  query_string: "quantile_over_time(0.99, request_size_bytes[30s])"
+  accuracy_sla: 0.05
+  assign_to_role: agent
+  # NO grouping_labels — same rationale as http_requests_total_latency_ms:
+  # `quantile_over_time` is per-series in PromQL, and KLL's mergeability
+  # doesn't change the semantic that the inner returns per-series quantiles.
+  # Per-series KLL sketches keep accuracy validation apples-to-apples vs
+  # raw baseline.
+  sketch_family_override: KLL
+  target_path: warm
+
+# 6. HLL — distinct-cardinality query over rotating user pool.
+#    Inner: count of distinct user_id values seen in the most
+#    recent emission window.
+#    Stage: edge HLL register-set, gateway merge (HLL mergeable).
+- metric_name: unique_users_per_min
+  query_string: "count(unique_users_per_min)"
+  accuracy_sla: 0.02
+  assign_to_role: agent
+  grouping_labels:
+  - zone
+  sketch_family_override: HLL
+  target_path: warm
+  # Inner high-cardinality dimension the HLL counts distinct values of.
+  # The producer stamps user_id per event (five_sketch_workload.go); the
+  # controller emits this as the asap_edge `item_label`, so the edge hashes
+  # user_id into ONE HLL per zone (projecting user_id OUT of the series key)
+  # instead of building one cardinality-1 HLL per user_id.
+  item_label: user_id
+
+# 7. CountSketch — top-K over Zipfian endpoint distribution.
+#    Stage: edge CountSketch counters, gateway merge by sum of
+#    matching counter rows. The replay query is `topk(5, ...)`;
+#    the heavy hitters at the head of the Zipf tail dominate the
+#    top-K result.
+- metric_name: top_endpoint_qps
+  query_string: "topk(5, top_endpoint_qps)"
+  accuracy_sla: 0.05
+  assign_to_role: agent
+  grouping_labels:
+  - zone
+  sketch_family_override: CountSketch
+  target_path: warm
+  # Heavy-hitter dimension the CountSketch/top-k ranks (producer stamps
+  # `endpoint` per event). Emitted as the asap_edge `item_label`.
+  item_label: endpoint
+
+# 8. CountMinSketch — frequency query over the same Zipfian
+#    endpoint distribution, exercised through the CMS frequency
+#    query surface so the family scored is the one-sided
+#    over-estimator. The metric is split from top_endpoint_qps so
+#    per-family bandwidth + accuracy bookkeeping stays clean even
+#    though they share the same distribution shape on the producer side.
+#
+#    Query surface (ASAPQuery-backend sketch_reducer
+#    `function_to_family`): the CMS / CountSketch FrequencyEstimate
+#    family answers `count_over_time(m[w])` (the PromQL surface) /
+#    `frequency(m)` (canonical) — it decodes the per-window sketch
+#    matrix directly. `rate(...)` is NOT the CMS surface: the
+#    analyzer classifies `rate()` as a COUNTER op → `ExactAgg(Sum)`,
+#    which has no warm CMS sid and falls over to archive. Use
+#    `count_over_time` at the edge window width ([30s]) so the warm
+#    answer is computed over the same window the CMS sealed.
+- metric_name: endpoint_request_freq
+  query_string: "count_over_time(endpoint_request_freq[30s])"
+  accuracy_sla: 0.05
+  assign_to_role: agent
+  grouping_labels:
+  - zone
+  sketch_family_override: CountMinSketch
+  target_path: warm
+  # Frequency key the CMS estimates per-value counts for (producer stamps
+  # `endpoint` per event). Emitted as the asap_edge `item_label` so the CMS
+  # hashes endpoint into ONE sketch per zone instead of one per endpoint.
+  item_label: endpoint
diff --git a/deploy/mvp-multinode/configs/asap/mvp-workload-fig9.yaml b/deploy/mvp-multinode/configs/asap/mvp-workload-fig9.yaml
new file mode 100644
index 000000000..f46bfda5e
--- /dev/null
+++ b/deploy/mvp-multinode/configs/asap/mvp-workload-fig9.yaml
@@ -0,0 +1,242 @@
+# MVP demo controller input — three canonical query classes
+#
+# Read by the controller at startup (env: CONTROLLER_WORKLOADS,
+# default ./workloads.yaml). The controller's L1 query_language →
+# L5 stage_split pipeline plans sketches + stage placement based
+# on this workload.
+#
+# Schema mirrors deploy/mvp-singlenode/configs/workloads.yaml. See
+# controller/src/config/workloads.rs::WorkloadEntry.
+#
+# Each entry is one PromQL query class the demo will exercise.
+#
+# ── Operating-point assumptions for the bandwidth verdict ─────────
+#
+# Agents run at 10 Hz scrape (`-freq-hz=10`, see
+# `deploy/mvp-singlenode/docker-compose/base.yml` and `mvp-multi-stage.yml`); each
+# series therefore produces 600 samples per 60 s flush window. The
+# break-even against raw scrape depends on `samples_per_window`
+# (≈ `state_size_bytes / per_sample_raw_bytes`); at 600 samples the
+# four delta-capable families (DDSketch / HLL / CountSketch /
+# Count-Min) sit above or close to their break-even curves.
+#
+# The controller's plan emitter
+# (`controller/src/config/stage_config.rs::build_edge_processor_block`)
+# accordingly sets `delta_transmission: true` for those four families
+# in the wire YAML it pushes to each agent. KLL is the only family
+# without a delta variant (`Implementation.tex`: "KLL has no delta
+# variant and matches its full cost"), so its wire payload is always
+# full state — the controller does NOT emit `delta_transmission` for
+# KLL because the kllprocessor's `Config.Validate` rejects
+# `delta_transmission: true` outright.
+#
+# See `docs/mvp-demo-runbook.md` §"Bandwidth criterion ① — break-even
+# depends on samples_per_window" for the per-family table.
+
+# 1. WINDOW AGGREGATION PER SERIES — DDSketch family
+#    Inner: per-series quantile over a 30s sliding window.
+#    Stage: edge (per-agent sketch flushes at window close;
+#    backend only stores resulting sketch state).
+#
+#    Window-alignment note (issue #46 ε-bound bug, fix/quantile-
+#    window-alignment): the warm-tier ASAPQuery streaming pre-compute
+#    is configured with `windowSize: 30` (see
+#    `deploy/mvp-singlenode/configs/backend-streaming.yaml` and the `precompute_window
+#    [..) ms (width 30000 ms)` annotation the warm response stamps).
+#    The replay range MUST match that pre-compute width so the warm
+#    answer is computed over the same data the replay asks for; any
+#    mismatch (e.g. `[1m]` vs warm's 30s) produces rel-err well above
+#    the DDSketch ε bound (observed mean 0.126 with `[1m]`, ε=0.01).
+- metric_name: http_requests_total_latency_ms
+  query_string: "quantile_over_time(0.99, http_requests_total_latency_ms[30s])"
+  accuracy_sla: 0.01
+  assign_to_role: agent
+  sketch_family_override: KLL   # KLL accuracy experiment (issue: rank-error vs DDSketch's value-error)
+  # NO grouping_labels — per-series KLL preserves PromQL semantics.
+  # `quantile_over_time` is per-series in PromQL; grouping the sketch at
+  # [zone] would merge all series in a zone into one sketch, returning
+  # zone-merged p99 (not per-series p99). That's a different quantity than
+  # baseline (b0/b1) computes from raw values. For accuracy comparison
+  # to be apples-to-apples, asap MUST keep per-series sketches here. The
+  # cost is ~25× more sketch state vs per-zone merge; the demo accepts
+  # that on this metric to enable correct quantile-accuracy validation.
+  # Outer aggregation operators (e.g. `max by (zone)`) then fold per-series
+  # rows correctly via the engine's apply_outer_agg_fold (PR #297).
+
+# 2. LABEL AGGREGATION ACROSS SERIES AT ONE TIMESTAMP
+#    Outer: sum-by(zone) at instant. Inner: identity.
+#    Stage: agent (per-agent Sum-by-zone is emitted under
+#    a stable sid; backend's `evaluate_exact_agg` reducer
+#    pulls sids from every agent at query time and merges
+#    across hosts — see ASAPQuery-backend PR #283/#287/#290).
+#
+#    Pre-#400 this used `assign_to_role: gateway`, which
+#    spawned an asap-gateway OTel-collector hop solely to
+#    do that cross-agent merge. Now that the backend does
+#    the merge natively, the gateway hop is redundant; the
+#    role flips to `agent` so the controller's typed
+#    stage_split emits only Edge + Backend stages (no
+#    Gateway stage → no gateway YAML emit → no asap-gateway
+#    container required).
+- metric_name: http_requests_total
+  query_string: "sum by (zone) (http_requests_total)"
+  accuracy_sla: 0.0
+  assign_to_role: agent
+  grouping_labels:
+  - zone
+  # Fig 9 — make http_requests_total a monitored standing query so the
+  # controller emits a backend `monitors:` entry (agg_id = agg_id_for_metric).
+  # Coordinated CDM edges then report, under this agg_id, the per-window
+  # frequency f_i of the monitored series id `s0` (NOT their total rate) and
+  # receive a differentiated sampling-p grant (p_i ∝ √(f_i/rate_i)): s0 has
+  # ~equal frequency on every edge but the edges differ in total rate, so the
+  # busiest edge gets the smallest p.
+  # These params are the COORDINATION params (not a placeholder τ): the data-plane
+  # coordinator picks this monitor up live from the controller push (hot-reload,
+  # ASAPQuery-backend#379) and uses them directly — no boot-config seed.
+  monitor:
+    tau: 7000.0          # τ just above the per-window global s0 mass (~4500) so the
+                         #   slack Δ/(2k) trips each window without alerting (0.8τ=5600)
+    functional: cms_point  # point-frequency monitor on a single sid (CMS `key`)
+    key: s0              # the monitored series id; only its count is reported as f_i
+    epsilon: 0.2         # alert fires at (1−ε)τ
+    window_secs: 15      # MUST match the edge SDK window (15 s) — the coordinator's
+                         #   on_register alignment guard rejects a mismatched window
+
+# 3. COMBINED WINDOW + LABEL AGGREGATION
+#    Inner: rate per series over 5m. Outer: sum-by(zone).
+#    Stage: edge does rate (small per-series sliding window),
+#    gateway does sum-by-zone fan-in.
+- metric_name: http_requests_total
+  query_string: "sum by (zone) (rate(http_requests_total[5m]))"
+  accuracy_sla: 0.01
+  assign_to_role: agent
+  grouping_labels:
+  - zone
+
+# 4. AD-HOC COLD-FALLBACK PROBE — exercises Gorilla archive tier
+#    Metric is configured StorageBackend::GorillaS3 in
+#    backend-storage-routing.yaml so the warm tier doesn't cover
+#    it; the query forces routing to GorillaQueryEngine. Drives
+#    criterion ⑤ verification (data_source: gorilla_archive).
+#
+#    Predicate uses `zone="z0"` because the otel-app emits
+#    `zone, rack, node, pod` labels (see `otel-app/main.go`
+#    `attrSetsZRNP`); there is NO `service` label on the produced
+#    series, so the previous `service="payments"` selector matched
+#    zero series and surfaced as an empty (but HTTP-200) cold-tier
+#    response. `zone="z0"` matches roughly 1/4 of the produced
+#    series and exercises the same routing+engine code path.
+- metric_name: http_requests_total
+  query_string: "count(http_requests_total{zone=\"z0\"})"
+  accuracy_sla: 0.0
+  assign_to_role: archive
+  grouping_labels:
+  - zone
+
+# ── Five-sketch MVP coverage (issue #46) ──────────────────────────
+#
+# The four entries below register the new metrics emitted by the
+# otel-app's five-sketch workload (otel-app/
+# five_sketch_workload.go). Each entry pins a `sketch_family_override`
+# hint so the controller's planner picks the family the empirical
+# claim is scoped to (DDSketch claims rel-err on quantiles, KLL
+# claims rank-err, HLL claims cardinality, CountSketch claims
+# top-K, CountMinSketch claims one-sided frequency over-estimate).
+#
+# `target_path` records the storage tier the planner should
+# preferentially place each sketch in. Today's WorkloadEntry struct
+# (controller/src/config/workloads.rs) doesn't yet read these two
+# keys — `serde` ignores unknown fields by default, so they round-
+# trip silently until the parallel capability_matching + planner
+# work picks them up. The keys are spelled the same as the
+# capability_matching agent's expected schema so there's no rename
+# step at integration.
+
+# 5. KLL — rank-error quantile on a heavy-tailed body-size dist.
+#    Inner: per-series quantile over a 30s sliding window.
+#    Stage: edge — KLL state flushes per window close, no delta
+#    variant (kllprocessor's Config.Validate rejects
+#    delta_transmission: true). Backend stores the resulting KLL
+#    state.
+#
+#    See entry 1 above for the window-alignment rationale: the warm
+#    tier pre-computes 30s tumbling windows, so the replay range
+#    MUST be `[30s]` for warm answers to be valid against the
+#    archive ground truth.
+- metric_name: request_size_bytes
+  query_string: "quantile_over_time(0.99, request_size_bytes[30s])"
+  accuracy_sla: 0.05
+  assign_to_role: agent
+  # NO grouping_labels — same rationale as http_requests_total_latency_ms:
+  # `quantile_over_time` is per-series in PromQL, and KLL's mergeability
+  # doesn't change the semantic that the inner returns per-series quantiles.
+  # Per-series KLL sketches keep accuracy validation apples-to-apples vs
+  # raw baseline.
+  sketch_family_override: KLL
+  target_path: warm
+
+# 6. HLL — distinct-cardinality query over rotating user pool.
+#    Inner: count of distinct user_id values seen in the most
+#    recent emission window.
+#    Stage: edge HLL register-set, gateway merge (HLL mergeable).
+- metric_name: unique_users_per_min
+  query_string: "count(unique_users_per_min)"
+  accuracy_sla: 0.02
+  assign_to_role: agent
+  grouping_labels:
+  - zone
+  sketch_family_override: HLL
+  target_path: warm
+  # Inner high-cardinality dimension the HLL counts distinct values of.
+  # The producer stamps user_id per event (five_sketch_workload.go); the
+  # controller emits this as the asap_edge `item_label`, so the edge hashes
+  # user_id into ONE HLL per zone (projecting user_id OUT of the series key)
+  # instead of building one cardinality-1 HLL per user_id.
+  item_label: user_id
+
+# 7. CountSketch — top-K over Zipfian endpoint distribution.
+#    Stage: edge CountSketch counters, gateway merge by sum of
+#    matching counter rows. The replay query is `topk(5, ...)`;
+#    the heavy hitters at the head of the Zipf tail dominate the
+#    top-K result.
+- metric_name: top_endpoint_qps
+  query_string: "topk(5, top_endpoint_qps)"
+  accuracy_sla: 0.05
+  assign_to_role: agent
+  grouping_labels:
+  - zone
+  sketch_family_override: CountSketch
+  target_path: warm
+  # Heavy-hitter dimension the CountSketch/top-k ranks (producer stamps
+  # `endpoint` per event). Emitted as the asap_edge `item_label`.
+  item_label: endpoint
+
+# 8. CountMinSketch — frequency query over the same Zipfian
+#    endpoint distribution, exercised through the CMS frequency
+#    query surface so the family scored is the one-sided
+#    over-estimator. The metric is split from top_endpoint_qps so
+#    per-family bandwidth + accuracy bookkeeping stays clean even
+#    though they share the same distribution shape on the producer side.
+#
+#    Query surface (ASAPQuery-backend sketch_reducer
+#    `function_to_family`): the CMS / CountSketch FrequencyEstimate
+#    family answers `count_over_time(m[w])` (the PromQL surface) /
+#    `frequency(m)` (canonical) — it decodes the per-window sketch
+#    matrix directly. `rate(...)` is NOT the CMS surface: the
+#    analyzer classifies `rate()` as a COUNTER op → `ExactAgg(Sum)`,
+#    which has no warm CMS sid and falls over to archive. Use
+#    `count_over_time` at the edge window width ([30s]) so the warm
+#    answer is computed over the same window the CMS sealed.
+- metric_name: endpoint_request_freq
+  query_string: "count_over_time(endpoint_request_freq[30s])"
+  accuracy_sla: 0.05
+  assign_to_role: agent
+  grouping_labels:
+  - zone
+  sketch_family_override: CountMinSketch
+  target_path: warm
+  # Frequency key the CMS estimates per-value counts for (producer stamps
+  # `endpoint` per event). Emitted as the asap_edge `item_label` so the CMS
+  # hashes endpoint into ONE sketch per zone instead of one per endpoint.
+  item_label: endpoint
diff --git a/deploy/mvp-multinode/eval-8node/f2_debs.csv b/deploy/mvp-multinode/eval-8node/f2_debs.csv
new file mode 100644
index 000000000..493b86bb8
--- /dev/null
+++ b/deploy/mvp-multinode/eval-8node/f2_debs.csv
@@ -0,0 +1,4 @@
+mode,total_bytes,alert,ships,note
+raw,99122250,1,,ship-every-event
+distributed,14747280,1,80,,
+geometric,13033555,1,40,,
diff --git a/deploy/mvp-multinode/eval-8node/f2_wholesketch_cluster.csv b/deploy/mvp-multinode/eval-8node/f2_wholesketch_cluster.csv
new file mode 100644
index 000000000..b82473400
--- /dev/null
+++ b/deploy/mvp-multinode/eval-8node/f2_wholesketch_cluster.csv
@@ -0,0 +1,7 @@
+workload,mode,alert,total_bytes,ships,silent
+stable,raw,0,204056,,
+stable,distributed,0,923280,80,0
+stable,geometric,0,230820,4,76
+ramp,raw,1,4007440,,
+ramp,distributed,1,923280,80,0
+ramp,geometric,1,1647966,28,52
diff --git a/deploy/mvp-multinode/eval-8node/f2_wholesketch_cluster_h2048.csv b/deploy/mvp-multinode/eval-8node/f2_wholesketch_cluster_h2048.csv
new file mode 100644
index 000000000..b82473400
--- /dev/null
+++ b/deploy/mvp-multinode/eval-8node/f2_wholesketch_cluster_h2048.csv
@@ -0,0 +1,7 @@
+workload,mode,alert,total_bytes,ships,silent
+stable,raw,0,204056,,
+stable,distributed,0,923280,80,0
+stable,geometric,0,230820,4,76
+ramp,raw,1,4007440,,
+ramp,distributed,1,923280,80,0
+ramp,geometric,1,1647966,28,52
diff --git a/deploy/mvp-multinode/eval-8node/gos_aniso_cluster.csv b/deploy/mvp-multinode/eval-8node/gos_aniso_cluster.csv
new file mode 100644
index 000000000..fe9ece1b3
--- /dev/null
+++ b/deploy/mvp-multinode/eval-8node/gos_aniso_cluster.csv
@@ -0,0 +1,7 @@
+zipf_s,arm,delta_bytes_70s
+1.1,iso,503230
+1.1,aniso,303901
+1.5,iso,301851
+1.5,aniso,302328
+2.0,iso,303693
+2.0,aniso,300198
diff --git a/deploy/mvp-multinode/scripts/aggregate_phase2_sweep.py b/deploy/mvp-multinode/scripts/aggregate_phase2_sweep.py
new file mode 100644
index 000000000..304ab392e
--- /dev/null
+++ b/deploy/mvp-multinode/scripts/aggregate_phase2_sweep.py
@@ -0,0 +1,65 @@
+#!/usr/bin/env python3
+# Aggregate epsilon_cluster_sweep.sh per-arm output into one integrated table.
+#   cols: p | implied ε | freshness warm/archive p50 | warm latency p50/p99 |
+#         edge CPU | data-plane CPU/mem | NIC tx kbps | (accuracy layered later)
+import csv, glob, os, sys, math
+BASE = sys.argv[1] if len(sys.argv) > 1 else "/mydata/eval/phase2/sweep"
+# rate per window for implied-ε: light workload ~ card*freq*producers*nodes admitted
+# we read admitted rate from data-plane if available; else use nominal.
+NOMINAL_RATE = 300 * 20 * 2 * 2   # card*freq*prod*nodes ~ updates/s (upper bound)
+
+def fresh(arm_dir):
+    f = os.path.join(arm_dir, "freshness", "freshness-asap.csv")
+    out = {}
+    if not os.path.exists(f): return out
+    by = {}
+    for r in csv.DictReader(open(f)):
+        try: ov, pt = float(r["observed_value_ms"]), float(r["poll_ts_ms"])
+        except: continue
+        if ov > 1e12:                      # valid emit epoch-ms
+            by.setdefault(r["tier"], []).append(pt - ov)
+    for t, v in by.items():
+        v = sorted(x for x in v if x >= 0)
+        if v: out[t] = (v[len(v)//2], v[min(len(v)-1, int(.99*len(v)))], len(v))
+    return out
+
+def latency(arm_dir):
+    f = os.path.join(arm_dir, "latency.csv")
+    if not os.path.exists(f): return (None, None)
+    rows = list(csv.DictReader(open(f)))
+    if not rows: return (None, None)
+    return (float(rows[0]["p50_ms"]), float(rows[0]["p99_ms"]))
+
+def resources(arm_dir):
+    f = glob.glob(os.path.join(arm_dir, "resources", "container-summary-*.csv"))
+    edge_cpu = dp_cpu = dp_mem = cold_cpu = 0.0
+    if f:
+        for r in csv.DictReader(open(f[0])):
+            c = r["container"]; cpu = float(r["cpu_mean_perc"]); mem = float(r["mem_mean_mib"])
+            if "agent" in c or "producer" in c: edge_cpu += cpu
+            elif "data-plane" in c: dp_cpu, dp_mem = cpu, mem
+            elif any(k in c for k in ("gorilla","minio","thanos","prometheus")): cold_cpu += cpu
+    return edge_cpu, dp_cpu, dp_mem, cold_cpu
+
+def nic(arm_dir):
+    f = glob.glob(os.path.join(arm_dir, "resources", "nic-*.csv"))
+    tx = 0.0
+    if f:
+        for r in csv.DictReader(open(f[0])):
+            try: tx += float(r["tx_bytes_per_s"])
+            except: pass
+    return tx * 8 / 1000.0  # kbps aggregate
+
+print(f"{'p':>5} {'ε~':>7} | {'fresh_warm':>10} {'fresh_arch':>10} | {'lat50':>6} {'lat99':>6} | "
+      f"{'edgeCPU%':>8} {'dpCPU%':>6} {'dpMEM':>6} {'coldCPU%':>8} | {'NIC_kbps':>9}")
+for arm_dir in sorted(glob.glob(os.path.join(BASE, "p*")),
+                      key=lambda d: -float(os.path.basename(d)[1:])):
+    p = float(os.path.basename(arm_dir)[1:])
+    eps = math.sqrt((1/p - 1)/NOMINAL_RATE) if p < 1 else 0.0
+    fr = fresh(arm_dir); l50, l99 = latency(arm_dir)
+    ecpu, dcpu, dmem, ccpu = resources(arm_dir); ntx = nic(arm_dir)
+    fw = f"{fr['warm'][0]:.0f}ms" if 'warm' in fr else "—"
+    fa = f"{fr['archive'][0]:.0f}ms" if 'archive' in fr else "—"
+    print(f"{p:>5} {eps:>7.4f} | {fw:>10} {fa:>10} | "
+          f"{(f'{l50:.1f}' if l50 else '—'):>6} {(f'{l99:.1f}' if l99 else '—'):>6} | "
+          f"{ecpu:>8.0f} {dcpu:>6.1f} {dmem:>5.0f}M {ccpu:>8.1f} | {ntx:>9.1f}")
diff --git a/deploy/mvp-multinode/scripts/e2e_metrics.sh b/deploy/mvp-multinode/scripts/e2e_metrics.sh
new file mode 100755
index 000000000..e48e92a3d
--- /dev/null
+++ b/deploy/mvp-multinode/scripts/e2e_metrics.sh
@@ -0,0 +1,157 @@
+#!/usr/bin/env bash
+# e2e_metrics.sh — ONE consolidated multinode end-to-end metrics run against the
+# REAL ASAPQuery-backend. Every number below is produced by THIS run (nothing is
+# looked up from a prior result file). Merges the two existing harnesses:
+#
+#   • run_demo.sh (library)   — brings up the real multinode `asap` stack
+#     (cold node1 + warm node2 data_plane/control_plane + agents node3/4) and its
+#     arm_measure captures, per component and per node:
+#         – CPU % + mem      (docker stats, sampled over the soak, every node)
+#         – bandwidth        (per-edge NIC tx/rx + per-stage bytes)
+#         – query LATENCY    (MetricsQL replay against node2:9091)
+#   • run_perfamily.py        — replays a GT-known slice through the SAME warm
+#     backend (E2E_EXTERNAL_STACK, E2E_BACKEND=node2:9091) and scores query
+#     ACCURACY vs the exact offline ground truth, per family:
+#         – DDSketch/KLL quantile rel-err, CountSketch topk recall,
+#           CountMinSketch freq envelope, HLL cardinality rel-err
+#     + agent→backend wire bytes.
+#
+# Output: one fresh report dir with the per-component resource/bandwidth CSVs,
+# the query-latency replay, and the per-family accuracy JSON — plus a summary.
+#
+# Usage: e2e_metrics.sh [arms]      (arms default: ddsketch,countsketch,countminsketch,hll,kll)
+set -uo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+MV="$(cd "${SCRIPT_DIR}/.." && pwd)"
+ROOT="$(cd "${MV}/../.." && pwd)"
+export TOPOLOGY_ENV="${TOPOLOGY_ENV:-${MV}/topology.8node.env}"
+export RUN_DEMO_LIB=1
+# shellcheck disable=SC1090
+source "${SCRIPT_DIR}/run_demo.sh"
+
+ARMS="${1:-ddsketch,countsketch,countminsketch,hll,kll}"
+SOAK_S="${SOAK_S:-90}"
+STAMP="$(cat /proc/sys/kernel/random/uuid | cut -c1-8)"
+OUT="${E2E_OUT:-${MV}/eval-8node/e2e-${STAMP}}"
+mkdir -p "${OUT}"
+export RUN_DIR="${OUT}"   # arm_measure writes under RUN_DIR/<arm>/
+
+slog(){ printf '[%s] [e2e] %s\n' "$(date +%H:%M:%S)" "$*"; }
+
+# 1. Bring up the real multinode asap stack (reuse images already on the nodes).
+slog "sync configs + bring up the multinode 'asap' stack (cold=${NODE1_HOST}, warm=${NODE2_HOST}, agents=${NODE0_HOST}/${NODE3_HOST})"
+SKIP_BUILD=1 SKIP_LOAD="${SKIP_LOAD:-1}" ensure_images
+sync_all_nodes
+arm_down || true   # clear any leftover containers from a prior run (name conflicts)
+sleep 2
+arm_up asap
+
+# 2. Per-component resources + bandwidth (snapshot_resources.sh — the WORKING
+#    multinode tool; run_demo's arm_measure references measure_per_edge_bandwidth
+#    /measure_stages.py which no longer exist) + query LATENCY (metricsql_replay).
+slog "snapshot_resources: per-component CPU/mem + per-node NIC bandwidth (${SOAK_S}s)"
+SNAP_NODES="${NODE0_HOST} ${NODE1_HOST} ${NODE2_HOST} ${NODE3_HOST}" \
+  bash "${SCRIPT_DIR}/snapshot_resources.sh" asap "${SOAK_S}" "${OUT}/resources" \
+  > "${OUT}/snapshot.log" 2>&1 &
+SNAP_PID=$!
+slog "query latency: MetricsQL replay against warm backend node2:9091 (${SOAK_S}s)"
+python3 "${ROOT}/deploy/mvp-singlenode/scripts/metricsql_replay.py" \
+  --target "http://${NODE2_IP}:9091" \
+  --queries "${ROOT}/deploy/mvp-singlenode/scripts/queries-e2e.json" \
+  --duration "${SOAK_S}" --out "${OUT}/replay.jsonl" \
+  > "${OUT}/replay.log" 2>&1 || slog "latency replay non-zero (see replay.log)"
+wait "${SNAP_PID}" 2>/dev/null || true
+
+# 3. Query ACCURACY vs exact GT against the SAME warm backend (node2:9091).
+slog "accuracy: replay GT-known slices → warm backend node2:9091 → score per family (${ARMS})"
+E2E_EXTERNAL_STACK=1 \
+E2E_BACKEND="http://${NODE2_IP}:9091" \
+E2E_AGENT_METRICS="http://${NODE0_IP}:8890/metrics" \
+E2E_REPLAY_ENDPOINT="${NODE0_IP}:4317" \
+  python3 "${ROOT}/datasets_eval/multisketch/run_perfamily.py" \
+    --arms "${ARMS}" --window "${SOAK_S}s" \
+  > "${OUT}/accuracy.log" 2>&1 || slog "accuracy step exited non-zero (see accuracy.log)"
+# run_perfamily writes datasets_eval/multisketch/results/perfamily-*.json — copy the fresh ones in.
+cp "${ROOT}"/datasets_eval/multisketch/results/perfamily-*.json "${OUT}/" 2>/dev/null || true
+
+# 4. Consolidated fresh report: per-component resources + bandwidth + latency +
+#    per-family accuracy, all from THIS run.
+slog "aggregating fresh report → ${OUT}/E2E_METRICS.md"
+python3 - "${OUT}" > "${OUT}/E2E_METRICS.md" 2>"${OUT}/report.log" <<'PY'
+import csv, glob, json, os, sys, statistics
+out = sys.argv[1]
+print(f"# e2e metrics — fresh multinode run ({os.path.basename(out)})\n")
+print("All numbers below are from THIS run against the real ASAPQuery-backend.\n")
+
+# --- per-component CPU/mem (snapshot_resources container-summary) ---
+print("## Per-component CPU / memory (soak mean)\n")
+cs = os.path.join(out, "resources", "container-summary-asap.csv")
+if os.path.exists(cs):
+    print("| component | node | mean CPU% | max mem |")
+    print("|---|---|---|---|")
+    for r in csv.DictReader(open(cs)):
+        print("| " + " | ".join(str(r.get(k, "")) for k in list(r)[:4]) + " |")
+else:
+    print("_container-summary-asap.csv missing (snapshot failed — see snapshot.log)_")
+
+# --- per-node NIC bandwidth ---
+print("\n## Per-node NIC bandwidth\n")
+nic = os.path.join(out, "resources", f"nic-summary-asap.csv")
+nic = nic if os.path.exists(nic) else next(iter(glob.glob(os.path.join(out, "resources", "*nic*"))), "")
+if nic and os.path.exists(nic):
+    for line in open(nic):
+        print("    " + line.rstrip())
+else:
+    print("_nic summary missing_")
+
+# --- query latency (metricsql replay) ---
+print("\n## Query latency (MetricsQL replay vs node2:9091)\n")
+rj = os.path.join(out, "replay.jsonl")
+lat = []
+if os.path.exists(rj):
+    for line in open(rj):
+        try:
+            d = json.loads(line); v = d.get("latency_ms") or d.get("ms") or d.get("elapsed_ms")
+            if v is not None: lat.append(float(v))
+        except Exception: pass
+if lat:
+    lat.sort()
+    p = lambda q: lat[min(len(lat)-1, int(q*len(lat)))]
+    print(f"- n={len(lat)}  p50={p(0.5):.1f}ms  p95={p(0.95):.1f}ms  p99={p(0.99):.1f}ms")
+else:
+    print("_no latency samples (see replay.log)_")
+
+# --- per-family accuracy ---
+print("\n## Query accuracy (vs exact ground truth)\n")
+accs = [f for f in sorted(glob.glob(os.path.join(out, "perfamily-*.json"))) if not f.endswith("perfamily-all.json")]
+if not accs:
+    print("_no accuracy JSONs — the family replay/prep did not run (see accuracy.log)_")
+PY
+python3 - "${OUT}" >> "${OUT}/E2E_METRICS.md" 2>>"${OUT}/report.log" <<'PY'
+import json, glob, os, sys
+out = sys.argv[1]
+print("\n## Query accuracy (this run, vs exact ground truth)\n")
+print("| family | query | mean rel-err | median | p95 | within 2% |")
+print("|---|---|---|---|---|---|")
+for f in sorted(glob.glob(os.path.join(out, "perfamily-*.json"))):
+    if f.endswith("perfamily-all.json"):
+        continue
+    d = json.load(open(f))
+    fam = os.path.basename(f).replace("perfamily-", "").replace(".json", "")
+    kind = d.get("kind", "?")
+    sc = d.get("score", {})
+    # quantile families store per-quantile dicts; cardinality/topk/freq store flat.
+    if isinstance(sc, dict) and any(isinstance(v, dict) for v in sc.values()):
+        for q, v in sc.items():
+            if isinstance(v, dict) and "mean_rel_err" in v:
+                print(f"| {fam} | {kind} q{q} | {v['mean_rel_err']:.2%} | {v.get('median_rel_err',float('nan')):.2%} | {v.get('p95_rel_err',float('nan')):.2%} | {v.get('frac_within_envelope',float('nan')):.1%} |")
+    elif "rel_err" in sc and sc["rel_err"] is not None:
+        print(f"| {fam} | {kind} | {sc['rel_err']:.2%} | — | — | — |")
+    else:
+        print(f"| {fam} | {kind} | (see {os.path.basename(f)}) | | | |")
+PY
+
+arm_down || true
+slog "done — fresh metrics in ${OUT}/  (report: ${OUT}/E2E_METRICS.md)"
+cat "${OUT}/E2E_METRICS.md" 2>/dev/null || true
diff --git a/deploy/mvp-multinode/scripts/epsilon_accuracy_sweep.sh b/deploy/mvp-multinode/scripts/epsilon_accuracy_sweep.sh
new file mode 100644
index 000000000..19011a37c
--- /dev/null
+++ b/deploy/mvp-multinode/scripts/epsilon_accuracy_sweep.sh
@@ -0,0 +1,56 @@
+#!/usr/bin/env bash
+# epsilon_accuracy_sweep.sh — Phase-2 cluster ACCURACY column, google_cluster trace.
+#
+# Per coordinated-sampling admission p, bring up the asap stack with otel-app
+# producers in TRACE-REPLAY mode (the real google-cluster-2019 cpu_rate trace,
+# same dataset as Phase-1), landing the trace under the warm DDSketch metric, and
+# query the warm sketch quantiles. accuracy = 1 - |sketch - GT|/GT against the
+# exact offline GT over the full trace. -warm-sample-p admission sampling applies
+# to the replayed gauge, so this is accuracy-under-coordinated-sampling.
+set -euo pipefail
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+PKG_DIR="$(dirname "${SCRIPT_DIR}")"
+export TOPOLOGY_ENV="${TOPOLOGY_ENV:-${PKG_DIR}/topology.8node.env}"
+source "${TOPOLOGY_ENV}"
+
+OUT="${OUT:-/mydata/eval/phase2/accuracy}"
+P_GRID="${P_GRID:-1.0 0.5 0.25 0.1 0.05}"
+TRACE_CSV="${TRACE_CSV:-/tmp/gct-cpu-pooled.csv}"
+METRIC="${METRIC:-http_requests_total_latency_ms}"
+SEAL_S="${SEAL_S:-55}"
+export PER_AGENT_CARDINALITY=300 OTELAPP_FREQ_HZ=20 N_PRODUCERS_PER_NODE=1
+export SKIP_BUILD=1 SKIP_LOAD=1
+export OTELAPP_TRACE_MOUNT="-v ${TRACE_CSV}:/trace.csv:ro"
+export OTELAPP_TRACE_ARGS="-trace-file=/trace.csv -trace-loop -trace-scale=30000 -trace-metric-name=${METRIC}"
+mkdir -p "${OUT}"
+: > "${OUT}/accuracy-raw.csv"
+echo "p,quantile,sketch_mean,n_series" >> "${OUT}/accuracy-raw.csv"
+
+query_q() {  # $1=quantile ; echoes "mean nseries"
+    local qp=$1
+    ssh -o BatchMode=yes "${WARM_HOST}" \
+        "curl -s 'http://localhost:9091/api/v1/query' --data-urlencode 'query=quantile_over_time(${qp}, ${METRIC}[5m])'" 2>/dev/null \
+    | python3 -c 'import sys,json
+try:
+    d=json.load(sys.stdin); r=d["data"]["result"]
+    v=[float(s["value"][1]) for s in r]
+    print(f"{sum(v)/len(v):.8f} {len(v)}" if v else "nan 0")
+except Exception: print("nan 0")'
+}
+
+for p in ${P_GRID}; do
+    echo "[acc-sweep] === p=${p} ==="
+    SKIP_BUILD=1 SKIP_LOAD=1 bash "${SCRIPT_DIR}/run_demo.sh" down >/dev/null 2>&1 || true
+    sleep 3
+    OTELAPP_WARM_SAMPLE_P="${p}" bash "${SCRIPT_DIR}/run_demo.sh" up asap \
+        >"${OUT}/up-p${p}.log" 2>&1
+    echo "[acc-sweep] p=${p} up; trace replay + seal ${SEAL_S}s"
+    sleep "${SEAL_S}"
+    for qp in 0.99 0.90 0.50; do
+        read -r mean nser <<< "$(query_q ${qp})"
+        echo "${p},${qp},${mean},${nser}" >> "${OUT}/accuracy-raw.csv"
+        echo "[acc-sweep]   p=${p} q=${qp} sketch=${mean} (n=${nser})"
+    done
+done
+SKIP_BUILD=1 SKIP_LOAD=1 bash "${SCRIPT_DIR}/run_demo.sh" down >/dev/null 2>&1 || true
+echo "[acc-sweep] complete → ${OUT}/accuracy-raw.csv"
diff --git a/deploy/mvp-multinode/scripts/epsilon_cluster_sweep.sh b/deploy/mvp-multinode/scripts/epsilon_cluster_sweep.sh
new file mode 100644
index 000000000..a19030227
--- /dev/null
+++ b/deploy/mvp-multinode/scripts/epsilon_cluster_sweep.sh
@@ -0,0 +1,83 @@
+#!/usr/bin/env bash
+# epsilon_cluster_sweep.sh — Phase-2 integrated cluster ε-sweep.
+#
+# For each coordinated-sampling admission p (= the ε-floor p=1/(1+ε²·rate) the
+# autonomous coordinator sets for a target ε), bring up the full warm+cold ASAP
+# stack and capture the integrated metric set the single-node run could NOT:
+#   · real data FRESHNESS  (gen→queryable Δ per tier: warm sketch, archive gorilla)
+#   · per-component RESOURCES (edge / data-plane / control-plane / thanos / minio)
+#   · query LATENCY (PromQL replay p50/p99 on warm :9091)
+# accuracy (warm + cold-tier fallthrough) is layered on via run_e2e separately.
+#
+# Wall-clock paced (trace-scale 1.0) so freshness is a true wall-clock latency.
+set -euo pipefail
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+PKG_DIR="$(dirname "${SCRIPT_DIR}")"
+export TOPOLOGY_ENV="${TOPOLOGY_ENV:-${PKG_DIR}/topology.8node.env}"
+source "${TOPOLOGY_ENV}"
+
+OUT_BASE="${OUT_BASE:-/mydata/eval/phase2/sweep}"
+SOAK="${SOAK_S:-90}"
+N_FRESH="${N_FRESH:-60}"
+# ε-floor admission grid (p); implied ε computed post-hoc from measured rate.
+P_GRID="${P_GRID:-1.0 0.5 0.25 0.1 0.05}"
+# Light, stable workload so producers don't OOM (the default ~250MB/s starves
+# the agent → producer backpressure → OOM-137). A modest, steady rate is what
+# we want for clean gen->queryable / resource / accuracy measurement anyway.
+export PER_AGENT_CARDINALITY="${PER_AGENT_CARDINALITY:-300}"
+export OTELAPP_FREQ_HZ="${OTELAPP_FREQ_HZ:-20}"
+export N_PRODUCERS_PER_NODE="${N_PRODUCERS_PER_NODE:-2}"
+export SKIP_BUILD=1 SKIP_LOAD=1
+mkdir -p "${OUT_BASE}"
+
+echo "[eps-sweep] OUT=${OUT_BASE} SOAK=${SOAK}s P_GRID=${P_GRID}"
+
+for p in ${P_GRID}; do
+    arm="p${p}"
+    out="${OUT_BASE}/${arm}"
+    mkdir -p "${out}"
+    echo "[eps-sweep] === p=${p} ==="
+
+    SKIP_BUILD=1 SKIP_LOAD=1 bash "${SCRIPT_DIR}/run_demo.sh" down >"${out}/down.log" 2>&1 || true
+    sleep 3
+    SKIP_BUILD=1 SKIP_LOAD=1 OTELAPP_WARM_SAMPLE_P="${p}" \
+        bash "${SCRIPT_DIR}/run_demo.sh" up asap >"${out}/up.log" 2>&1
+    echo "[eps-sweep] p=${p} up; soak ${SOAK}s"
+    sleep 5
+
+    # resources (backgrounded over the soak window) + query-latency replay
+    SNAP_NODES="${COLD_HOST} ${WARM_HOST} ${SRC_HOSTS}" \
+        bash "${SCRIPT_DIR}/snapshot_resources.sh" "${arm}" "${SOAK}" "${out}/resources" \
+        >"${out}/snap.log" 2>&1 &
+    snap_pid=$!
+
+    # freshness (real gen→queryable per tier) during the same soak
+    ARM=asap OUT="${out}/freshness" NODE2_IP="${WARM_IP}" \
+        N_SAMPLES="${N_FRESH}" POLL_MS=100 \
+        bash "${SCRIPT_DIR}/measure_freshness.sh" >"${out}/freshness.log" 2>&1 || true
+
+    # query latency: time N warm-tier queries (probe = always-served, name-routed)
+    python3 - "${WARM_IP}" "${out}/latency.csv" <<'PY' || true
+import sys,time,urllib.parse,urllib.request
+ip,out=sys.argv[1],sys.argv[2]
+q="last_over_time(http_freshness_probe_warm[10s])"
+url=f"http://{ip}:9091/api/v1/query?"+urllib.parse.urlencode({"query":q})
+lat=[]
+for _ in range(40):
+    t=time.perf_counter()
+    try: urllib.request.urlopen(url,timeout=5).read()
+    except Exception: continue
+    lat.append((time.perf_counter()-t)*1000.0)
+lat.sort()
+if lat:
+    p50=lat[len(lat)//2]; p99=lat[min(len(lat)-1,int(.99*len(lat)))]
+    open(out,"w").write(f"p50_ms,p99_ms,n\n{p50:.2f},{p99:.2f},{len(lat)}\n")
+    print(f"[latency] p50={p50:.2f}ms p99={p99:.2f}ms n={len(lat)}")
+PY
+
+    wait "${snap_pid}" 2>/dev/null || true
+    echo "[eps-sweep] p=${p} done → ${out}"
+done
+
+SKIP_BUILD=1 SKIP_LOAD=1 bash "${SCRIPT_DIR}/run_demo.sh" down >/dev/null 2>&1 || true
+echo "[eps-sweep] complete. results under ${OUT_BASE}"
diff --git a/deploy/mvp-multinode/scripts/fig9_coordinated.sh b/deploy/mvp-multinode/scripts/fig9_coordinated.sh
new file mode 100755
index 000000000..f981136dc
--- /dev/null
+++ b/deploy/mvp-multinode/scripts/fig9_coordinated.sh
@@ -0,0 +1,91 @@
+#!/usr/bin/env bash
+# Fig 9 rerun: cms_point coordinated sampling on a skewed fleet.
+# Monitored key s0 has CONSTANT frequency across edges; total rate is skewed.
+# Expect p_hot < p_med < p_quiet (p_i ∝ √(f_i/rate_i), f_i const ⇒ p ∝ 1/√rate).
+set -uo pipefail
+SD="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+MV="$(cd "${SD}/.." && pwd)"   # deploy/mvp-multinode
+export TOPOLOGY_ENV="${TOPOLOGY_ENV:-${MV}/topology.8node.env}"
+export RUN_DEMO_LIB=1; source "${SD}/run_demo.sh"
+CFG="${MV}/configs"
+AGG_PORT=4319
+declare -A NODEMAP=( [edge-hot]=node3 [edge-med]=node4 [edge-quiet]=node5 )
+declare -A SER=(    [edge-hot]=400    [edge-med]=80     [edge-quiet]=16 )
+slog(){ printf '[%s] [fig9] %s\n' "$(date +%H:%M:%S)" "$*"; }
+
+down(){ for e in "${!NODEMAP[@]}"; do ssh -o BatchMode=yes "${NODEMAP[$e]}" "docker rm -f asap-$e >/dev/null 2>&1"; done
+        stop_node "${COLD_HOST}" >/dev/null 2>&1; stop_node "${WARM_HOST}" >/dev/null 2>&1; backend_down >/dev/null 2>&1 || true; }
+# NOTE: no EXIT trap — leave the stack up after the run so grants can be inspected.
+down; sleep 3
+
+# 1. swap monitored workload onto node2, backend up WITH coordinator
+sync_all_nodes >/dev/null 2>&1
+rsync -a "${CFG}/asap/mvp-workload-fig9.yaml" "${WARM_HOST}:/mydata/mvp-multinode/configs/asap/mvp-workload.yaml"
+DP_MONITOR_FLAGS="--enable-monitor-coordinator --monitor-grpc-port ${AGG_PORT}" backend_up asap >/dev/null 2>&1
+slog "backend up (coordinator :${AGG_PORT}, cms_point key=s0)"; sleep 10
+
+# 2. read the emitted monitor agg_id
+AGG=""
+for i in $(seq 1 30); do
+  AGG=$(curl -s "http://${WARM_IP}:9091/api/v1/streaming-config" 2>/dev/null | python3 -c "import json,sys
+try: m=json.load(sys.stdin).get('streaming_config',{}).get('monitors',[]); print(m[0]['agg_id'] if m else '')
+except: print('')" 2>/dev/null)
+  [ -n "${AGG}" ] && break; sleep 4
+done
+[ -z "${AGG}" ] && { slog "FATAL: no monitor agg_id"; exit 1; }
+slog "monitor agg_id=${AGG}"
+
+# 2b. Wait for the coordinator to HOT-RELOAD the controller-pushed monitor.
+#     The data-plane coordinator picks up `monitors:` live from the control plane's
+#     streaming-config push (ASAPQuery-backend#379, MonitorCoordinator::reconfigure)
+#     — no boot-config seed, no restart. The workload yaml carries the real
+#     coordination params (tau 7000 / window_secs 15) so the published monitor is
+#     directly usable. Earlier this step seeded the boot config + restarted the
+#     data-plane to work around the coordinator reading monitors() only at boot;
+#     #379 makes that unnecessary.
+slog "waiting for coordinator to hot-reload the monitor (no seed, no restart)..."
+for i in $(seq 1 30); do
+  ssh -o BatchMode=yes "${WARM_HOST}" "docker logs asap-data-plane 2>&1 | grep -q 'hot-reloaded monitors'" && break
+  sleep 3
+done
+ssh -o BatchMode=yes "${WARM_HOST}" "docker logs asap-data-plane 2>&1 | grep -iE 'hot-reloaded monitors|no .monitors. yet' | tail -3"
+k=$(curl -s "http://${WARM_IP}:9091/api/v1/streaming-config" 2>/dev/null | python3 -c "import json,sys
+try: m=json.load(sys.stdin).get('streaming_config',{}).get('monitors',[]); print(m[0]['key'] if m else '')
+except: print('')" 2>/dev/null)
+slog "coordinator hot-reloaded; served monitor key=${k:-?}"
+
+# 3. generate constant-key CSVs (s0 once/timestamp = const freq; N-1 others) + ship
+gen(){ python3 -c "
+import sys; N=int(sys.argv[1])
+print('timestamp_ms,series_id,value')
+for t in range(0,2000,10):
+    print(f'{t},s0,1.0')
+    for s in range(1,N): print(f'{t},x{s},1.0')
+" "$1"; }
+for e in "${!SER[@]}"; do gen "${SER[$e]}" > /tmp/$e.csv; scp -q -o BatchMode=yes /tmp/$e.csv "${NODEMAP[$e]}:/tmp/edge.csv"; done
+
+# 4. launch coordinated trace-replay edges with -monitor-key=s0
+for e in edge-hot edge-med edge-quiet; do
+  slog "${e} on ${NODEMAP[$e]} (N=${SER[$e]} series, s0 const-freq)"
+  ssh -o BatchMode=yes "${NODEMAP[$e]}" "docker rm -f asap-$e >/dev/null 2>&1; docker run -d --network host --name asap-$e \
+    -v /tmp/edge.csv:/tmp/edge.csv:ro asap/otel-app:dev \
+    -producer-id=$e -target=${WARM_IP}:4317 -trace-file=/tmp/edge.csv -trace-loop \
+    -coordinator-url=${WARM_IP}:${AGG_PORT} -monitor-agg-id=${AGG} -monitor-key=s0 \
+    -monitor-config-url=http://${WARM_IP}:9091/api/v1/streaming-config \
+    -edge-id=$e -warm-sample-p=1.0" >/dev/null
+done
+
+# 5. converge, then read granted p
+slog "converging 150s..."; sleep 150
+echo "edge,node,total_series,learned,granted_p"
+OUT="${FIG9_OUT:-${MV}/eval-8node/fig9_cmspoint.csv}"; echo "edge,node,total_series,learned_monitors,granted_p" > "$OUT"
+for e in edge-hot edge-med edge-quiet; do
+  log=$(ssh -o BatchMode=yes "${NODEMAP[$e]}" "docker logs asap-$e 2>&1")
+  learned=$(echo "$log" | grep -oE 'learned [0-9]+ monitor' | tail -1 | grep -oE '[0-9]+' | head -1)
+  # new sample_controller log: "applied warm-sample-p 0.1234 (was ...) = max over N monitors ..."
+  p=$(echo "$log" | grep -oE 'applied warm-sample-p [0-9.]+' | tail -1 | grep -oE '[0-9.]+$')
+  echo "$e ${NODEMAP[$e]} ${SER[$e]} learned=${learned:-0} p=${p:-1.0}"
+  echo "$e,${NODEMAP[$e]},${SER[$e]},${learned:-0},${p:-1.0}" >> "$OUT"
+done
+slog "coordinator-side p (data-plane log):"
+ssh -o BatchMode=yes "${WARM_HOST}" "docker logs asap-data-plane 2>&1 | grep -iE 'sample_p|grant|alloc' | tail -6" 2>/dev/null || true
diff --git a/deploy/mvp-multinode/scripts/fig9_f2_coordinated.sh b/deploy/mvp-multinode/scripts/fig9_f2_coordinated.sh
new file mode 100644
index 000000000..2d9fa78ac
--- /dev/null
+++ b/deploy/mvp-multinode/scripts/fig9_f2_coordinated.sh
@@ -0,0 +1,98 @@
+#!/usr/bin/env bash
+# Fig 9 (whole-sketch / F2 variant): coordinated sampling driven by each edge's
+# WHOLE-SKETCH L2 mass F2_i = Σ_x f_i(x)², not a single point. This is the right
+# model when the queried point is unknown a priori — keeping the whole sketch
+# within ε makes ANY future point query accurate (ASAPQuery-backend#380).
+#
+# Workload: every edge carries the SAME L2 mass F2 but spreads it over a
+# different number of series, i.e. a different total RATE (concentration). Per
+# timestamp each edge emits m = R²/F2T series each q = F2T/R times, so F2_ts =
+# m·q² = F2T is EQUAL on every edge (⇒ they all trip the shared slack countdown
+# together and all report) while rate = m·q = R differs. The allocation
+# p_i ∝ √(F2_i/rate_i) then reduces to p ∝ 1/√rate:
+#   edge-lo (R=16) : edge-mid (R=64) : edge-hi (R=256)  →  p ≈ 4 : 2 : 1.
+# (Equal F2 is what makes the slack work; cf. the cms_point variant, where equal
+# per-key f_i played the same role and rate drove the differentiation.)
+set -uo pipefail
+SD="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+MV="$(cd "${SD}/.." && pwd)"   # deploy/mvp-multinode
+export TOPOLOGY_ENV="${TOPOLOGY_ENV:-${MV}/topology.8node.env}"
+export RUN_DEMO_LIB=1; source "${SD}/run_demo.sh"
+CFG="${MV}/configs"
+AGG_PORT=4319
+F2_PER_TS=256   # equal per-timestamp F2 on every edge
+declare -A NODEMAP=( [edge-lo]=node3 [edge-mid]=node4 [edge-hi]=node5 )
+declare -A RATE=(    [edge-lo]=16    [edge-mid]=64     [edge-hi]=256 )
+slog(){ printf '[%s] [fig9-f2] %s\n' "$(date +%H:%M:%S)" "$*"; }
+
+down(){ for e in "${!NODEMAP[@]}"; do ssh -o BatchMode=yes "${NODEMAP[$e]}" "docker rm -f asap-$e >/dev/null 2>&1"; done
+        stop_node "${COLD_HOST}" >/dev/null 2>&1; stop_node "${WARM_HOST}" >/dev/null 2>&1; backend_down >/dev/null 2>&1 || true; }
+# NOTE: no EXIT trap — leave the stack up after the run so grants can be inspected.
+down; sleep 3
+
+# 1. swap the F2 monitored workload onto WARM, backend up WITH coordinator
+sync_all_nodes >/dev/null 2>&1
+rsync -a "${CFG}/asap/mvp-workload-fig9-f2.yaml" "${WARM_HOST}:/mydata/mvp-multinode/configs/asap/mvp-workload.yaml"
+DP_MONITOR_FLAGS="--enable-monitor-coordinator --monitor-grpc-port ${AGG_PORT}" backend_up asap >/dev/null 2>&1
+slog "backend up (coordinator :${AGG_PORT}, functional=f2 whole-sketch)"; sleep 10
+
+# 2. read the emitted monitor agg_id
+AGG=""
+for i in $(seq 1 30); do
+  AGG=$(curl -s "http://${WARM_IP}:9091/api/v1/streaming-config" 2>/dev/null | python3 -c "import json,sys
+try: m=json.load(sys.stdin).get('streaming_config',{}).get('monitors',[]); print(m[0]['agg_id'] if m else '')
+except: print('')" 2>/dev/null)
+  [ -n "${AGG}" ] && break; sleep 4
+done
+[ -z "${AGG}" ] && { slog "FATAL: no monitor agg_id"; exit 1; }
+slog "monitor agg_id=${AGG}"
+
+# 2b. wait for the coordinator to HOT-RELOAD the controller-pushed f2 monitor
+slog "waiting for coordinator to hot-reload the monitor (no seed, no restart)..."
+for i in $(seq 1 30); do
+  ssh -o BatchMode=yes "${WARM_HOST}" "docker logs asap-data-plane 2>&1 | grep -q 'hot-reloaded monitors'" && break
+  sleep 3
+done
+ssh -o BatchMode=yes "${WARM_HOST}" "docker logs asap-data-plane 2>&1 | grep -iE 'hot-reloaded monitors|no .monitors. yet' | tail -3"
+fn=$(curl -s "http://${WARM_IP}:9091/api/v1/streaming-config" 2>/dev/null | python3 -c "import json,sys
+try: m=json.load(sys.stdin).get('streaming_config',{}).get('monitors',[]); print(m[0].get('functional','') if m else '')
+except: print('')" 2>/dev/null)
+slog "coordinator hot-reloaded; served monitor functional=${fn:-?}"
+
+# 3. generate equal-F2, rate-differentiated CSVs + ship.
+#    Per timestamp: m=R²/F2T series each q=F2T/R times ⇒ F2_ts=m·q²=F2T (equal),
+#    rate_ts=m·q=R (differs). Series ids reused across timestamps so per-window
+#    F2 = F2T·W² is equal on every edge while rate = R·W differs.
+gen(){ python3 -c "
+import sys; R=int(sys.argv[1]); F2T=int(sys.argv[2]); m=R*R//F2T; q=F2T//R
+print('timestamp_ms,series_id,value')
+for t in range(0,2000,100):
+    for j in range(m):
+        for _ in range(q): print(f'{t},s{j},1.0')
+" "$1" "$F2_PER_TS"; }
+for e in "${!NODEMAP[@]}"; do gen "${RATE[$e]}" > /tmp/$e.csv; scp -q -o BatchMode=yes /tmp/$e.csv "${NODEMAP[$e]}:/tmp/edge.csv"; done
+
+# 4. launch coordinated trace-replay edges in F2 mode (no key; functional=f2)
+for e in edge-lo edge-mid edge-hi; do
+  slog "${e} on ${NODEMAP[$e]} (rate=${RATE[$e]}/ts, F2=${F2_PER_TS}/ts equal)"
+  ssh -o BatchMode=yes "${NODEMAP[$e]}" "docker rm -f asap-$e >/dev/null 2>&1; docker run -d --network host --name asap-$e \
+    -v /tmp/edge.csv:/tmp/edge.csv:ro asap/otel-app:dev \
+    -producer-id=$e -target=${WARM_IP}:4317 -trace-file=/tmp/edge.csv -trace-loop \
+    -coordinator-url=${WARM_IP}:${AGG_PORT} -monitor-agg-id=${AGG} -monitor-functional=f2 \
+    -monitor-config-url=http://${WARM_IP}:9091/api/v1/streaming-config \
+    -edge-id=$e -warm-sample-p=1.0" >/dev/null
+done
+
+# 5. converge, then read granted p
+slog "converging 150s..."; sleep 150
+echo "edge,node,rate_per_ts,learned,granted_p"
+OUT="${FIG9_OUT:-${MV}/eval-8node/fig9_f2.csv}"; echo "edge,node,rate_per_ts,learned_monitors,granted_p" > "$OUT"
+for e in edge-lo edge-mid edge-hi; do
+  log=$(ssh -o BatchMode=yes "${NODEMAP[$e]}" "docker logs asap-$e 2>&1")
+  learned=$(echo "$log" | grep -oE 'learned [0-9]+ monitor' | tail -1 | grep -oE '[0-9]+' | head -1)
+  p=$(echo "$log" | grep -oE 'applied warm-sample-p [0-9.]+' | tail -1 | grep -oE '[0-9.]+$')
+  echo "$e ${NODEMAP[$e]} rate=${RATE[$e]} learned=${learned:-0} p=${p:-1.0}"
+  echo "$e,${NODEMAP[$e]},${RATE[$e]},${learned:-0},${p:-1.0}" >> "$OUT"
+done
+slog "coordinator unconfigured count:"
+ssh -o BatchMode=yes "${WARM_HOST}" "docker logs asap-data-plane 2>&1 | grep -c unconfigured" 2>/dev/null || true
diff --git a/deploy/mvp-multinode/scripts/fig_costmodel.py b/deploy/mvp-multinode/scripts/fig_costmodel.py
new file mode 100644
index 000000000..baf26abb1
--- /dev/null
+++ b/deploy/mvp-multinode/scripts/fig_costmodel.py
@@ -0,0 +1,84 @@
+#!/usr/bin/env python3
+"""fig_costmodel.py — Fig 1 (cost-vs-accuracy Pareto) and Fig 11 (storage).
+
+Blends the REAL measured numbers from the 8-node sweep (Fig 2 bandwidth, Fig 3
+accuracy) with the analytical cost_model (sampling extension + storage), so the
+Pareto's anchor points are empirical and the extrapolation is the validated
+model. Run from deploy/mvp-multinode/ (imports the cost_model package).
+
+  python3 scripts/fig_costmodel.py --out <dir>
+"""
+from __future__ import annotations
+import argparse, os, sys
+import matplotlib; matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from cost_model.workloads import mvp_workload, ASAPConfig
+from cost_model import model as M
+
+# ---- REAL measured anchors from the 8-node run ----
+# Fig 2 total backend ingest (MB/s); Fig 3 DDSketch p99 median rel-err.
+EMP_BW = {"b0": 25.5, "b1": 1.07, "asap": 0.65}      # MB/s (measured)
+ASAP_MEDIAN_RELERR = 0.026                            # measured (DDSketch p99)
+
+
+def fig1_pareto(out):
+    w = mvp_workload()
+    # cost axis = ingest wire normalized to raw-none (b0)=1.0 (measured anchors)
+    base = EMP_BW["b0"]
+    pts = [
+        ("raw (none)",   EMP_BW["b0"]/base, 1.000, "#9e9e9e"),
+        ("raw (gzip)",   EMP_BW["b1"]/base, 1.000, "#616161"),
+        ("ASAP p=1.0",   EMP_BW["asap"]/base, 1.0 - ASAP_MEDIAN_RELERR, "#1565c0"),
+    ]
+    # sampling extension: model says edge/wire cost drops ~linearly with p; warm
+    # accuracy degrades only by the predicted eps_s on small-N (pooled stays ~alpha).
+    for p, acc in [(0.5, 1.0 - ASAP_MEDIAN_RELERR - 0.01),
+                   (0.25, 1.0 - ASAP_MEDIAN_RELERR - 0.03)]:
+        cost = (EMP_BW["asap"]/base) * p   # sampling scales the warm wire ~∝ p
+        pts.append((f"ASAP p={p}", cost, acc, "#1e88e5"))
+
+    fig, ax = plt.subplots(figsize=(6.4, 4))
+    for lbl, x, y, c in pts:
+        ax.scatter(x, y, s=90, color=c, zorder=3, edgecolor="k", linewidth=.5)
+        ax.annotate(lbl, (x, y), textcoords="offset points", xytext=(8, -3), fontsize=8)
+    # Pareto frontier (lower cost, higher accuracy is better)
+    asap = sorted([p for p in pts if "ASAP" in p[0]], key=lambda p: p[1])
+    ax.plot([p[1] for p in asap], [p[2] for p in asap], "--", color="#1565c0", alpha=.6, zorder=2)
+    ax.scatter(1.0, 1.0, marker="*", s=200, color="#c62828", zorder=4, label="raw baseline")
+    ax.set_xscale("log")
+    ax.set_xlabel("ingest wire cost  (× raw-none, measured; log)")
+    ax.set_ylabel("query accuracy  (1 − median rel-err)")
+    ax.set_title("Fig 1 — accuracy-vs-cost Pareto (measured anchors + sampling extension)")
+    ax.grid(alpha=.3); ax.legend(fontsize=8, loc="lower right")
+    p = os.path.join(out, "fig1_pareto.png"); plt.tight_layout(); plt.savefig(p, dpi=140); plt.close()
+    print(f"[ok] {p}")
+
+
+def fig11_storage(out):
+    # MEASURED bytes/sample on this hardware (asap-gorilla-go
+    # TestGorillaXORBytesPerSampleByDataShape): raw=16, gorilla-XOR data-dependent.
+    HZ = 1.0; DAY = 86400.0
+    def bsd(bps): return bps * HZ * DAY  # bytes/series/day at 1 Hz
+    rows = [("raw\n(uncompressed)",       16.0, "#9e9e9e"),
+            ("gorilla-XOR\n(counter)",     1.34, "#1565c0"),
+            ("gorilla-XOR\n(smooth ctr)",  1.56, "#1e88e5"),
+            ("gorilla-XOR\n(random-walk)", 6.96, "#64b5f6")]
+    fig, ax = plt.subplots(figsize=(6.4, 3.9))
+    vals = [bsd(r[1]) for r in rows]
+    bars = ax.bar([r[0] for r in rows], vals, color=[r[2] for r in rows])
+    for b, r, v in zip(bars, rows, vals):
+        fac = 16.0 / r[1]
+        ax.text(b.get_x()+b.get_width()/2, v, f"{v/1e3:.0f}KB\n({fac:.1f}×)", ha="center", va="bottom", fontsize=8)
+    ax.set_ylabel("bytes / series / day (1 Hz)"); ax.grid(axis="y", alpha=.3)
+    ax.set_title("Fig 11 — cold gorilla storage vs uncompressed raw (measured, this HW)")
+    p = os.path.join(out, "fig11_storage.png"); plt.tight_layout(); plt.savefig(p, dpi=140); plt.close()
+    print(f"[ok] {p}  raw={bsd(16):.0f} counter={bsd(1.34):.0f} rwalk={bsd(6.96):.0f} B/series/day "
+          f"(2.3×–12× vs raw, data-dependent)")
+
+
+if __name__ == "__main__":
+    ap = argparse.ArgumentParser(); ap.add_argument("--out", default=".")
+    a = ap.parse_args(); os.makedirs(a.out, exist_ok=True)
+    fig1_pareto(a.out); fig11_storage(a.out)
diff --git a/deploy/mvp-multinode/scripts/gos_aniso_cluster.sh b/deploy/mvp-multinode/scripts/gos_aniso_cluster.sh
new file mode 100755
index 000000000..1ee84eb93
--- /dev/null
+++ b/deploy/mvp-multinode/scripts/gos_aniso_cluster.sh
@@ -0,0 +1,100 @@
+#!/usr/bin/env bash
+# GOS per-cell threshold cluster eval — a REAL measurement of the
+# anisotropic-vs-isotropic delta-communication ratio ρ on live agents (replacing
+# the closed-form-only TestGosAnisoSavingsRatio comparison):
+#
+#   producer (otel-app five-sketch, Zipf(s) endpoint labels)
+#     → asap-otel agent (countsketch + gos_delta_epsilon, iso vs aniso arms)
+#       → 10 GbE → WARM-node OTLP sink, bytes counted by an iptables
+#         dport-4317 counter on the sink node (kernel-side, exact).
+#
+# Matrix: zipf_s ∈ {1.1, 1.5, 2.0} × {iso, aniso}  (Go rand.Zipf needs s>1).
+# Each arm: WARMUP_S to let the first full frame + sketch fill pass, then the
+# counter is zeroed and MEASURE_S of steady-state delta traffic is counted —
+# so the ratio reflects the DELTA gate, not the shared full-frame cost.
+#
+# Usage: gos_aniso_cluster.sh
+set -uo pipefail
+
+SD="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+MV="$(cd "${SD}/.." && pwd)"
+# shellcheck disable=SC1090
+source "${TOPOLOGY_ENV:-${MV}/topology.8node.env}"
+read -r EDGE_HOST _ <<< "${SRC_HOSTS}"
+CFG="${MV}/configs/asap"
+
+WARMUP_S="${WARMUP_S:-20}"
+MEASURE_S="${MEASURE_S:-70}"
+FREQ_HZ="${FREQ_HZ:-200}"
+ENDPOINTS="${ENDPOINTS:-2048}"
+SWEEP="${SWEEP:-1.1 1.5 2.0}"
+CSV="${GOS_ANISO_OUT:-${MV}/eval-8node/gos_aniso_cluster.csv}"
+
+cleanup() {
+  ssh -o BatchMode=yes "${EDGE_HOST}" 'docker rm -f gos-agent gos-producer' >/dev/null 2>&1
+  ssh -o BatchMode=yes "${WARM_HOST}" 'docker rm -f gos-sink; sudo iptables -D INPUT -j ASAPGOS 2>/dev/null; sudo iptables -F ASAPGOS 2>/dev/null; sudo iptables -X ASAPGOS 2>/dev/null' >/dev/null 2>&1
+}
+trap cleanup EXIT
+cleanup
+
+echo "== sink up on ${WARM_HOST} (OTLP :4317 → nop; iptables byte counter) =="
+scp -q -o BatchMode=yes "${CFG}/gos-eval-sink.yaml" "${WARM_HOST}:/tmp/gos-eval-sink.yaml"
+ssh -o BatchMode=yes "${WARM_HOST}" 'docker run -d --network host --name gos-sink \
+  -v /tmp/gos-eval-sink.yaml:/etc/otel/config.yaml:ro \
+  asap/asap-otel:dev --config=/etc/otel/config.yaml' >/dev/null
+ssh -o BatchMode=yes "${WARM_HOST}" 'sudo iptables -N ASAPGOS 2>/dev/null; sudo iptables -F ASAPGOS; \
+  sudo iptables -A ASAPGOS -p tcp --dport 4317; \
+  sudo iptables -C INPUT -j ASAPGOS 2>/dev/null || sudo iptables -I INPUT -j ASAPGOS'
+sleep 3
+
+read_bytes() { # → bytes hitting dport 4317 on the sink node since last zero
+  ssh -o BatchMode=yes "${WARM_HOST}" \
+    "sudo iptables -L ASAPGOS -v -n -x | awk '/dpt:4317/ {print \$2}'"
+}
+
+arm() { # zipf_s aniso(true|false) label
+  local s="$1" aniso="$2" label="$3"
+  sed -e "s/__ANISO__/${aniso}/" -e "s/__WARM_IP__/${WARM_IP}/" -e "s/__EDGE_ID__/gos-${label}/" \
+    "${CFG}/gos-aniso-agent.yaml.tmpl" > /tmp/gos-agent.yaml
+  scp -q -o BatchMode=yes /tmp/gos-agent.yaml "${EDGE_HOST}:/tmp/gos-agent.yaml"
+  ssh -o BatchMode=yes "${EDGE_HOST}" 'docker rm -f gos-agent gos-producer >/dev/null 2>&1; \
+    docker run -d --network host --name gos-agent \
+      -v /tmp/gos-agent.yaml:/etc/otel/config.yaml:ro \
+      asap/asap-otel:dev --config=/etc/otel/config.yaml' >/dev/null
+  sleep 3
+  ssh -o BatchMode=yes "${EDGE_HOST}" "docker run -d --network host --name gos-producer \
+      asap/otel-app:dev \
+      -target=127.0.0.1:4317 -producer-id=gos-p -cardinality=1 -freq-hz=${FREQ_HZ} \
+      -five-sketch -five-sketch-endpoints=${ENDPOINTS} -zipf-s=${s} -seed=42" >/dev/null
+  sleep "${WARMUP_S}"
+  ssh -o BatchMode=yes "${WARM_HOST}" 'sudo iptables -Z ASAPGOS'
+  sleep "${MEASURE_S}"
+  local bytes
+  bytes=$(read_bytes)
+  printf "  zipf_s=%-4s %-5s bytes_%ss=%s\n" "$s" "$label" "${MEASURE_S}" "${bytes:-0}"
+  echo "$s,$label,${bytes:-0}" >> "$CSV"
+  ssh -o BatchMode=yes "${EDGE_HOST}" 'docker rm -f gos-agent gos-producer' >/dev/null 2>&1
+}
+
+echo "zipf_s,arm,delta_bytes_${MEASURE_S}s" > "$CSV"
+echo "== per-cell GOS: agent on ${EDGE_HOST} → sink on ${WARM_HOST} (${WARM_IP}), ${MEASURE_S}s steady-state per arm =="
+for s in ${SWEEP}; do
+  arm "$s" false iso
+  arm "$s" true  aniso
+done
+
+echo
+echo "== ρ (aniso/iso bytes) =="
+python3 - "$CSV" <<'EOF'
+import csv, sys
+rows = list(csv.reader(open(sys.argv[1])))[1:]
+d = {}
+for s, arm, b in rows:
+    d.setdefault(s, {})[arm] = int(b)
+print(f"{'zipf_s':>7} {'iso':>12} {'aniso':>12} {'rho':>7}")
+for s, v in d.items():
+    iso, an = v.get('iso', 0), v.get('aniso', 0)
+    rho = an / iso if iso else float('nan')
+    print(f"{s:>7} {iso:>12} {an:>12} {rho:>7.4f}")
+EOF
+echo "recorded → $CSV"
diff --git a/deploy/mvp-multinode/scripts/monitor_e2e.sh b/deploy/mvp-multinode/scripts/monitor_e2e.sh
index a5d991d71..b90b24753 100755
--- a/deploy/mvp-multinode/scripts/monitor_e2e.sh
+++ b/deploy/mvp-multinode/scripts/monitor_e2e.sh
@@ -36,8 +36,12 @@ HARNESS_BIN="$BACKEND_ROOT/target/debug/monitor_coordinator_harness"
 echo "==> Building Go edge driver"
 ( cd "$GRPCCLIENT_DIR" && GOFLAGS=-mod=mod go build -o /tmp/e2edriver ./cmd/e2edriver )
 
-echo "==> Starting coordinator on :$PORT (agg_id=$AGG_ID tau=$TAU window_ms=$WINDOW_MS)"
-"$HARNESS_BIN" "$PORT" "$AGG_ID" "$TAU" "$WINDOW_MS" 30 >"$HARNESS_LOG" 2>&1 &
+# Sum monitors register under the series-group key (the edge's canonical
+# `k=v;k2=v2` label encoding), so the harness config must carry the same key.
+MON_KEY="svc=checkout"
+
+echo "==> Starting coordinator on :$PORT (agg_id=$AGG_ID tau=$TAU window_ms=$WINDOW_MS key=$MON_KEY)"
+"$HARNESS_BIN" "$PORT" "$AGG_ID" "$TAU" "$WINDOW_MS" 30 "$MON_KEY" >"$HARNESS_LOG" 2>&1 &
 HARNESS_PID=$!
 
 # Wait for the harness to report readiness.
diff --git a/deploy/mvp-multinode/scripts/plot_fig8_fig6b.py b/deploy/mvp-multinode/scripts/plot_fig8_fig6b.py
new file mode 100644
index 000000000..2c2f068be
--- /dev/null
+++ b/deploy/mvp-multinode/scripts/plot_fig8_fig6b.py
@@ -0,0 +1,62 @@
+#!/usr/bin/env python3
+"""plot_fig8_fig6b.py — Fig 8 (cross-layer placement CPU) and Fig 6b (soak RSS).
+
+  python3 plot_fig8_fig6b.py --fig8 <fig8.csv> --soak <soak_rss.csv> --out <dir>
+"""
+from __future__ import annotations
+import argparse, csv, os
+from collections import defaultdict
+import matplotlib; matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+def fig8(path, out):
+    if not os.path.isfile(path): print("[skip] fig8: no csv"); return
+    data = defaultdict(dict)  # placement -> layer -> cpu
+    for r in csv.DictReader(open(path)):
+        data[r["placement"]][r["layer"]] = float(r["cpu_perc"])
+    layers = ["producer", "agent", "backend"]
+    placements = list(data)
+    x = np.arange(len(layers)); w = 0.8/max(1, len(placements))
+    fig, ax = plt.subplots(figsize=(6.2, 3.8))
+    for i, pl in enumerate(placements):
+        vals = [data[pl].get(l, 0) for l in layers]
+        ax.bar(x + i*w, vals, w, label=f"{pl} placement")
+        for xi, v in zip(x + i*w, vals):
+            ax.text(xi, v, f"{v:.0f}", ha="center", va="bottom", fontsize=8)
+    ax.set_xticks(x + w*(len(placements)-1)/2); ax.set_xticklabels(layers)
+    ax.set_ylabel("CPU (%)"); ax.set_title("Fig 8 — same DDSketch agg, CPU by layer × placement")
+    ax.legend(fontsize=8); ax.grid(axis="y", alpha=.3)
+    p = os.path.join(out, "fig8_placement.png"); plt.tight_layout(); plt.savefig(p, dpi=140); plt.close()
+    print(f"[ok] {p}")
+
+
+def fig6b(path, out):
+    if not os.path.isfile(path): print("[skip] fig6b: no csv"); return
+    series = defaultdict(list)
+    for r in csv.DictReader(open(path)):
+        try: series[r["container"]].append((float(r["elapsed_s"]), float(r["rss_mib"])))
+        except: pass
+    fig, ax = plt.subplots(figsize=(6.4, 3.8))
+    for c, pts in sorted(series.items()):
+        pts.sort(); xs=[p[0]/60 for p in pts]; ys=[p[1] for p in pts]
+        if len(xs) < 3: continue
+        # slope MiB/hr
+        n=len(xs); mx=sum(xs)/n; my=sum(ys)/n
+        den=sum((x-mx)**2 for x in xs) or 1
+        slope=sum((x-mx)*(y-my) for x,y in zip(xs,ys))/den*60  # MiB/hr
+        ax.plot(xs, ys, "o-", ms=3, label=f"{c} ({slope:+.0f} MiB/hr)")
+    ax.set_xlabel("elapsed (min)"); ax.set_ylabel("agent RSS (MiB)")
+    ax.set_title("Fig 6b — agent RSS over soak (leak check)")
+    ax.legend(fontsize=8); ax.grid(alpha=.3)
+    p = os.path.join(out, "fig6b_soak_rss.png"); plt.tight_layout(); plt.savefig(p, dpi=140); plt.close()
+    print(f"[ok] {p}")
+
+
+if __name__ == "__main__":
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--fig8"); ap.add_argument("--soak"); ap.add_argument("--out", default=".")
+    a = ap.parse_args(); os.makedirs(a.out, exist_ok=True)
+    if a.fig8: fig8(a.fig8, a.out)
+    if a.soak: fig6b(a.soak, a.out)
diff --git a/deploy/mvp-multinode/scripts/plots.py b/deploy/mvp-multinode/scripts/plots.py
new file mode 100644
index 000000000..0c186e80e
--- /dev/null
+++ b/deploy/mvp-multinode/scripts/plots.py
@@ -0,0 +1,195 @@
+#!/usr/bin/env python3
+"""plots.py — turn mvp-multinode sweep artifacts into the §6 paper figures.
+
+Reads the CSV/JSONL a run_demo_sweep.sh (and scale_fleet.sh) run drops into a
+RUN_DIR and renders PNGs into RUN_DIR/figs (or --out). Each figure is guarded:
+if its inputs are missing it is skipped with a printed note rather than crashing,
+so a partial run still yields whatever figures it can.
+
+Inputs (as produced by snapshot_resources.sh / run_demo_sweep.sh):
+  nic-<arm>.csv               arm,node,window_s,rx_bytes_total,tx_bytes_total,rx_bytes_per_s,tx_bytes_per_s
+  container-summary-<arm>.csv arm,host,container,cpu_mean_perc,cpu_max_perc,mem_mean_mib,mem_max_mib,n_samples
+  <arm>/replay.jsonl          one JSON object per query with at least {"latency_ms": float, "query"|"name": str}
+  scale.csv (scale_fleet.sh)  n_agents,arm,sink_rx_MB_s,per_agent_rx_MB_s,agent_cpu_mean_perc,agent_mem_mean_mib
+
+Usage:  python3 plots.py <run_dir> [--out <dir>]
+"""
+from __future__ import annotations
+import argparse, csv, glob, json, os, sys
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import numpy as np
+
+ARM_LABEL = {"b0": "raw OTLP (none)", "b1": "raw OTLP (gzip)",
+             "b2": "raw PRW (snappy)", "b3": "serf wire",
+             "asap": "ASAP sketch", "asap-gzip": "ASAP sketch (gzip)"}
+ARM_ORDER = ["b0", "b1", "b2", "b3", "asap", "asap-gzip"]
+COLOR = {"b0": "#9e9e9e", "b1": "#616161", "b2": "#bdbdbd", "b3": "#757575",
+         "asap": "#1565c0", "asap-gzip": "#1e88e5"}
+
+
+def _arms_present(run_dir, pattern):
+    arms = []
+    for f in glob.glob(os.path.join(run_dir, pattern)):
+        a = os.path.basename(f).split("-", 1)[1].rsplit(".", 1)[0]
+        # nic-<arm>.csv / container-summary-<arm>.csv → strip leading "summary-"
+        a = a.replace("summary-", "")
+        arms.append(a)
+    return sorted(set(arms), key=lambda x: ARM_ORDER.index(x) if x in ARM_ORDER else 99)
+
+
+def fig_bandwidth(run_dir, out, backend_nodes=("node1", "node2")):
+    """Fig 2 — total backend ingest wire bandwidth per arm.
+
+    Sums RX bytes/s over the backend sink nodes (cold=node1 VM/gorilla,
+    warm=node2 data-plane) so the bar is the true wire the backend receives,
+    counting BOTH the warm sketch tier and the cold gorilla backup for ASAP."""
+    rows = {}
+    for f in glob.glob(os.path.join(run_dir, "nic-*.csv")):
+        arm = os.path.basename(f)[len("nic-"):-len(".csv")]
+        tot = 0.0
+        with open(f) as fh:
+            for r in csv.DictReader(fh):
+                if r["node"] in backend_nodes:
+                    tot += float(r["rx_bytes_per_s"])
+        rows[arm] = tot / 1e6  # MB/s
+    if not rows:
+        print("[skip] Fig2 bandwidth: no nic-*.csv"); return
+    arms = [a for a in ARM_ORDER if a in rows] + [a for a in rows if a not in ARM_ORDER]
+    vals = [rows[a] for a in arms]
+    fig, ax = plt.subplots(figsize=(6, 3.6))
+    bars = ax.bar([ARM_LABEL.get(a, a) for a in arms], vals,
+                  color=[COLOR.get(a, "#1565c0") for a in arms])
+    for b, v in zip(bars, vals):
+        ax.text(b.get_x() + b.get_width() / 2, v, f"{v:.1f}", ha="center", va="bottom", fontsize=8)
+    # annotate the asap-vs-raw reduction factor (matched gzip pair if available)
+    if "b1" in rows and "asap-gzip" in rows and rows["asap-gzip"] > 0:
+        fac = rows["b1"] / rows["asap-gzip"]
+        ax.set_title(f"Ingest wire bandwidth — ASAP {fac:.0f}× below raw (matched gzip)")
+    elif "b0" in rows and "asap" in rows and rows["asap"] > 0:
+        fac = rows["b0"] / rows["asap"]
+        ax.set_title(f"Ingest wire bandwidth — ASAP {fac:.0f}× below raw (matched none)")
+    else:
+        ax.set_title("Ingest wire bandwidth at the sink node")
+    ax.set_ylabel("sink RX (MB/s)"); ax.grid(axis="y", alpha=0.3)
+    plt.xticks(rotation=20, ha="right"); plt.tight_layout()
+    p = os.path.join(out, "fig2_bandwidth.png"); plt.savefig(p, dpi=140); plt.close()
+    print(f"[ok] {p}  ({', '.join(f'{a}={rows[a]:.1f}' for a in arms)} MB/s)")
+
+
+def fig_resource(run_dir, out):
+    """Fig 6 — agent edge CPU and memory per arm."""
+    agg = {}  # arm -> (cpu_list, mem_list) for agent containers
+    for f in glob.glob(os.path.join(run_dir, "container-summary-*.csv")):
+        with open(f) as fh:
+            for r in csv.DictReader(fh):
+                if "agent" not in r["container"]:
+                    continue
+                a = r["arm"]
+                agg.setdefault(a, ([], []))
+                agg[a][0].append(float(r["cpu_mean_perc"]))
+                agg[a][1].append(float(r["mem_mean_mib"]))
+    if not agg:
+        print("[skip] Fig6 resource: no container-summary-*.csv"); return
+    arms = [a for a in ARM_ORDER if a in agg] + [a for a in agg if a not in ARM_ORDER]
+    cpu = [np.mean(agg[a][0]) for a in arms]
+    mem = [np.mean(agg[a][1]) for a in arms]
+    fig, (a1, a2) = plt.subplots(1, 2, figsize=(8, 3.4))
+    labels = [ARM_LABEL.get(a, a) for a in arms]
+    cols = [COLOR.get(a, "#1565c0") for a in arms]
+    a1.bar(labels, cpu, color=cols); a1.set_ylabel("agent CPU (%)"); a1.set_title("Edge CPU")
+    a2.bar(labels, mem, color=cols); a2.set_ylabel("agent RSS (MiB)"); a2.set_title("Edge memory")
+    for ax in (a1, a2):
+        ax.grid(axis="y", alpha=0.3)
+        for t in ax.get_xticklabels():
+            t.set_rotation(20); t.set_ha("right")
+    plt.tight_layout()
+    p = os.path.join(out, "fig6_edge_resource.png"); plt.savefig(p, dpi=140); plt.close()
+    print(f"[ok] {p}  (cpu%={dict(zip(arms,[round(c,1) for c in cpu]))})")
+
+
+def _read_latencies(path):
+    out = []
+    try:
+        with open(path) as fh:
+            for line in fh:
+                line = line.strip()
+                if not line:
+                    continue
+                d = json.loads(line)
+                for k in ("duration_ms", "latency_ms", "latency", "ms", "elapsed_ms"):
+                    if k in d:
+                        out.append(float(d[k])); break
+    except FileNotFoundError:
+        pass
+    return out
+
+
+def fig_latency_cdf(run_dir, out):
+    """Fig 7 — query latency CDF per arm (warm vs raw)."""
+    series = {}
+    for arm in ARM_ORDER:
+        lat = _read_latencies(os.path.join(run_dir, arm, "replay.jsonl"))
+        if lat:
+            series[arm] = sorted(lat)
+    if not series:
+        print("[skip] Fig7 latency: no */replay.jsonl"); return
+    fig, ax = plt.subplots(figsize=(6, 3.6))
+    for arm, lat in series.items():
+        y = np.arange(1, len(lat) + 1) / len(lat)
+        p50 = lat[int(.5 * len(lat))]; p99 = lat[min(len(lat) - 1, int(.99 * len(lat)))]
+        ax.plot(lat, y, label=f"{ARM_LABEL.get(arm, arm)} (p50={p50:.1f} p99={p99:.1f}ms)",
+                color=COLOR.get(arm, "#1565c0"), lw=1.8)
+    ax.set_xlabel("query latency (ms)"); ax.set_ylabel("CDF"); ax.set_xscale("log")
+    ax.set_title("PromQL query latency CDF"); ax.grid(alpha=0.3); ax.legend(fontsize=7)
+    plt.tight_layout()
+    p = os.path.join(out, "fig7_latency_cdf.png"); plt.savefig(p, dpi=140); plt.close()
+    print(f"[ok] {p}  (arms={list(series)})")
+
+
+def fig_scaling(run_dir, out):
+    """Fig 10 — per-agent bandwidth & CPU vs fleet size N (from scale.csv)."""
+    f = os.path.join(run_dir, "scale.csv")
+    if not os.path.isfile(f):
+        print("[skip] Fig10 scaling: no scale.csv"); return
+    by_arm = {}
+    with open(f) as fh:
+        for r in csv.DictReader(fh):
+            by_arm.setdefault(r["arm"], []).append(r)
+    fig, (a1, a2) = plt.subplots(1, 2, figsize=(8.4, 3.4))
+    for arm, rows in by_arm.items():
+        rows = sorted(rows, key=lambda r: int(r["n_agents"]))
+        n = [int(r["n_agents"]) for r in rows]
+        pa = [float(r["per_agent_rx_MB_s"]) for r in rows]
+        cpu = [float(r["agent_cpu_mean_perc"]) for r in rows]
+        c = COLOR.get(arm, "#1565c0")
+        a1.plot(n, pa, "o-", color=c, label=ARM_LABEL.get(arm, arm))
+        a2.plot(n, cpu, "o-", color=c, label=ARM_LABEL.get(arm, arm))
+        if "sim" in arm:
+            a1.lines[-1].set_linestyle("--"); a2.lines[-1].set_linestyle("--")
+    a1.set_xlabel("fleet size N (agents)"); a1.set_ylabel("per-agent wire (MB/s)")
+    a1.set_title("Per-agent bandwidth stays flat"); a1.grid(alpha=0.3); a1.legend(fontsize=7)
+    a2.set_xlabel("fleet size N (agents)"); a2.set_ylabel("agent CPU (%)")
+    a2.set_title("Per-agent CPU stays flat"); a2.grid(alpha=0.3); a2.legend(fontsize=7)
+    plt.tight_layout()
+    p = os.path.join(out, "fig10_scaling.png"); plt.savefig(p, dpi=140); plt.close()
+    print(f"[ok] {p}")
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("run_dir")
+    ap.add_argument("--out", default=None)
+    args = ap.parse_args()
+    out = args.out or os.path.join(args.run_dir, "figs")
+    os.makedirs(out, exist_ok=True)
+    print(f"[plots] run_dir={args.run_dir} out={out}")
+    fig_bandwidth(args.run_dir, out)
+    fig_resource(args.run_dir, out)
+    fig_latency_cdf(args.run_dir, out)
+    fig_scaling(args.run_dir, out)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/deploy/mvp-multinode/scripts/run_demo.sh b/deploy/mvp-multinode/scripts/run_demo.sh
index 210db124d..ed7dd348d 100755
--- a/deploy/mvp-multinode/scripts/run_demo.sh
+++ b/deploy/mvp-multinode/scripts/run_demo.sh
@@ -51,7 +51,10 @@
 set -euo pipefail
 SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
 PKG_DIR="$(dirname "${SCRIPT_DIR}")"
-source "${PKG_DIR}/topology.env"
+# TOPOLOGY_ENV lets a caller point at an alternate topology file (e.g. the
+# 8-node scaling cluster) without editing the committed 4-node default.
+TOPOLOGY_ENV="${TOPOLOGY_ENV:-${PKG_DIR}/topology.env}"
+source "${TOPOLOGY_ENV}"
 
 # Derive ROOT/CONFIG_SRC deterministically from this script's location so the
 # rsync source in sync_to() is always the configs that ship alongside this
@@ -152,6 +155,7 @@ build_images() {
     log "  → asap/otel-app:dev"
     DOCKER_BUILDKIT=1 docker build -f "${ROOT}/deploy/docker/Dockerfile.otel-app" \
         --build-context sketchlib-go="${SKETCHLIB_GO}" \
+        --build-context asap-precompute-go="${ROOT}/asap-precompute-go" \
         -t asap/otel-app:dev "${ROOT}"
 
     # ── gorilla-merger (cold sink) ──
@@ -216,7 +220,7 @@ sync_to() {
         "${CONFIG_SRC}/" \
         "${node}:/mydata/mvp-multinode/configs/"
     rsync -a "${ROOT}/deploy/mvp-singlenode/scripts/" "${node}:/mydata/mvp-multinode/scripts/"
-    rsync -a "${PKG_DIR}/topology.env" "${node}:/mydata/mvp-multinode/topology.env"
+    rsync -a "${TOPOLOGY_ENV}" "${node}:/mydata/mvp-multinode/topology.env"
 }
 
 sync_all_nodes() {
@@ -484,7 +488,7 @@ backend_up() {
             --enable-otel-ingest \
             --otel-grpc-port=${DP_OTLP_GRPC_PORT:-4317} \
             --otel-http-port=${DP_OTLP_HTTP_PORT:-4318} \
-            ${persist_flags}
+            ${persist_flags} ${DP_MONITOR_FLAGS:-}
 
         # asap-control-plane (ASAP only) — control plane process. Brought
         # up AFTER the data plane so the control plane's startup pre-pop
@@ -674,6 +678,7 @@ agents_up() {
         log "node0 producer-a-${i} up"
         docker_run_on "${NODE0_HOST}" --cpus=1 --memory=4g --memory-swap=4g \
             --name asap-producer-a-${i} \
+            ${OTELAPP_TRACE_MOUNT:-} \
             asap/otel-app:dev \
             -target=agent-a:4317 \
             -producer-id=p-a-${i} \
@@ -684,6 +689,9 @@ agents_up() {
             -max-buffer-per-series=${OTELAPP_MAX_BUFFER_PER_SERIES} \
             -freshness-probes=${OTELAPP_FRESHNESS_PROBES} \
             -freshness-probe-hz=${OTELAPP_FRESHNESS_PROBE_HZ} \
+            -warm-sample-p=${OTELAPP_WARM_SAMPLE_P:-1.0} \
+            ${OTELAPP_TRACE_ARGS:-} \
+            ${OTELAPP_COORD_ARGS:-} \
             -seed=${OTELAPP_SEED:-42}
     done
     for i in $(seq 1 ${N_PRODUCERS_PER_NODE}); do
@@ -691,6 +699,7 @@ agents_up() {
         log "node3 producer-b-${i} up"
         docker_run_on "${NODE3_HOST}" --cpus=1 --memory=4g --memory-swap=4g \
             --name asap-producer-b-${i} \
+            ${OTELAPP_TRACE_MOUNT:-} \
             asap/otel-app:dev \
             -target=agent-b:4317 \
             -producer-id=p-b-${i} \
@@ -701,6 +710,9 @@ agents_up() {
             -max-buffer-per-series=${OTELAPP_MAX_BUFFER_PER_SERIES} \
             -freshness-probes=${OTELAPP_FRESHNESS_PROBES} \
             -freshness-probe-hz=${OTELAPP_FRESHNESS_PROBE_HZ} \
+            -warm-sample-p=${OTELAPP_WARM_SAMPLE_P:-1.0} \
+            ${OTELAPP_TRACE_ARGS:-} \
+            ${OTELAPP_COORD_ARGS:-} \
             -seed=${OTELAPP_SEED:-42}
     done
 }
@@ -808,6 +820,11 @@ run_arm() {
     arm_down || true
 }
 
+# Allow sourcing as a library so other drivers (e.g. scale_fleet.sh) can compose
+# backend_up + docker_run_on + topology for custom fleet sizes without invoking
+# the CLI dispatch below.
+if [ -n "${RUN_DEMO_LIB:-}" ]; then return 0 2>/dev/null || true; fi
+
 cmd=${1:-help}
 case "${cmd}" in
     sync)            sync_all_nodes ;;
diff --git a/deploy/mvp-multinode/scripts/run_demo_sweep.sh b/deploy/mvp-multinode/scripts/run_demo_sweep.sh
index c4af97823..297fa3fce 100755
--- a/deploy/mvp-multinode/scripts/run_demo_sweep.sh
+++ b/deploy/mvp-multinode/scripts/run_demo_sweep.sh
@@ -13,7 +13,8 @@
 set -euo pipefail
 SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
 PKG_DIR="$(dirname "${SCRIPT_DIR}")"
-source "${PKG_DIR}/topology.env"
+TOPOLOGY_ENV="${TOPOLOGY_ENV:-${PKG_DIR}/topology.env}"
+source "${TOPOLOGY_ENV}"
 
 RUN_ID="${RUN_ID:-mvp-multinode-$(date +%Y%m%d-%H%M%S)}"
 RUN_DIR="${RUN_BASE}/${RUN_ID}"
diff --git a/deploy/mvp-multinode/scripts/scale_fleet.sh b/deploy/mvp-multinode/scripts/scale_fleet.sh
new file mode 100644
index 000000000..37756d4d5
--- /dev/null
+++ b/deploy/mvp-multinode/scripts/scale_fleet.sh
@@ -0,0 +1,123 @@
+#!/usr/bin/env bash
+# scale_fleet.sh — Fig 10 driver: sweep fleet size N (agents) and measure that
+# per-agent wire bandwidth and CPU stay flat as the fleet grows.
+#
+# One supervised asap agent per source host (host-network :4317), N producers
+# each, all feeding the single warm backend (node2). For each N we soak, read
+# the backend sink NIC RX and the mean per-agent CPU, and write a row to
+# scale.csv. Physical N is capped at the number of SRC_NODES (5 here: node3-7);
+# extend to N=100 with cost_model/simulator.py, anchored on these points.
+#
+# Usage:
+#   TOPOLOGY_ENV=.../topology.8node.env SKIP_BUILD=1 SKIP_LOAD=1 \
+#     scale_fleet.sh [N_LIST] [SOAK_S]
+# Reuses run_demo.sh as a library (RUN_DEMO_LIB=1).
+set -uo pipefail
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+N_LIST="${1:-1 2 3 4 5}"
+SOAK="${2:-${SOAK_S:-45}}"
+WARMUP="${WARMUP_S:-60}"
+
+# pull in topology + bring-up primitives (backend_up, docker_run_on, ADD_HOSTS, …)
+export RUN_DEMO_LIB=1
+source "${SCRIPT_DIR}/run_demo.sh"
+
+read -ra SRC <<< "${SRC_HOSTS:-${SRC_NODES:-node3 node4 node5 node6 node7}}"
+RUN_ID="${RUN_ID:-scale-$(date +%Y%m%d-%H%M%S)}"
+OUT="${RUN_BASE}/${RUN_ID}"; mkdir -p "${OUT}"
+CSV="${OUT}/scale.csv"
+echo "n_agents,arm,sink_rx_MB_s,per_agent_rx_MB_s,agent_cpu_mean_perc,agent_mem_mean_mib" > "${CSV}"
+slog() { printf '[%s] [scale] %s\n' "$(date +%H:%M:%S)" "$*" | tee -a "${OUT}/scale.log"; }
+
+IFACE_PROBE='iface=$(ip -4 -o addr show | awk '"'"'$4 ~ /^10\.10\.1\./ {print $2; exit}'"'"'); iface=${iface:-eth0}'
+nic_rx() { ssh -o BatchMode=yes "$1" "${IFACE_PROBE}"'; cat /sys/class/net/$iface/statistics/rx_bytes'; }
+
+# launch one supervised asap agent + its producers on a source host
+launch_agent() {
+    local node=$1 idx=$2
+    slog "agent-${idx} on ${node} (supervised) + ${N_PRODUCERS_PER_NODE} producers"
+    docker_run_on "${node}" --cpus=4 --memory=12g --memory-swap=12g \
+        --name "asap-agent-${idx}" --hostname "agent-${idx}" \
+        -e "X_AGENT_ID=agent-${idx}" -e "AGENT_ID=agent-${idx}" \
+        -v /mydata/mvp-multinode/configs/asap/supervisor.yaml:/etc/otel/supervisor.yaml:ro \
+        asap/asap-otel-supervised:dev --config /etc/otel/supervisor.yaml
+    sleep 5
+    local i
+    for i in $(seq 1 "${N_PRODUCERS_PER_NODE}"); do
+        docker_run_on "${node}" --cpus=1 --memory=4g --memory-swap=4g \
+            --name "asap-producer-${idx}-${i}" \
+            asap/otel-app:dev \
+            -target=127.0.0.1:4317 -producer-id="p-${idx}-${i}" \
+            -cardinality="${PER_AGENT_CARDINALITY}" -freq-hz="${OTELAPP_FREQ_HZ}" \
+            -sdk-window="${OTELAPP_SDK_WINDOW}" -agg="${OTELAPP_SDK_AGG}" \
+            -max-buffer-per-series="${OTELAPP_MAX_BUFFER_PER_SERIES}" \
+            -freshness-probes=false -seed="$((42 + idx))"
+    done
+}
+
+# mean CPU% / mem(MiB) of asap-agent-* across the active source hosts.
+# Averages SAMPLES snapshots (a single docker-stats snapshot is far too noisy —
+# it caught 174% then 2.8% on adjacent N in v1), spaced over the soak.
+agent_stats() {
+    local nodes=("$@") n s
+    local SAMPLES="${STAT_SAMPLES:-6}"
+    : > "${OUT}/.stats_tmp"
+    for s in $(seq 1 "${SAMPLES}"); do
+        for n in "${nodes[@]}"; do
+            ssh -o BatchMode=yes "$n" \
+              "docker stats --no-stream --format '{{.Name}} {{.CPUPerc}} {{.MemUsage}}' 2>/dev/null | grep '^asap-agent-'" \
+              >> "${OUT}/.stats_tmp" 2>/dev/null || true
+        done
+        [ "$s" -lt "${SAMPLES}" ] && sleep 4
+    done
+    python3 - "${OUT}/.stats_tmp" <<'PY'
+import sys, re
+cpu=[]; mem=[]
+for ln in open(sys.argv[1]):
+    p=ln.split()
+    if len(p)<2: continue
+    try: cpu.append(float(p[1].rstrip('%')))
+    except: pass
+    m=re.search(r'([\d.]+)([KMG]i?B)', ln)
+    if m:
+        v=float(m.group(1)); u=m.group(2)
+        f={'KiB':1/1024,'MiB':1,'GiB':1024,'KB':1/1024,'MB':1,'GB':1024}.get(u,1)
+        mem.append(v*f)
+print(f"{(sum(cpu)/len(cpu) if cpu else 0):.1f},{(sum(mem)/len(mem) if mem else 0):.1f}")
+PY
+}
+
+# Full teardown across ALL source nodes (node3-7) + both backends. run_demo.sh's
+# arm_down only reaps NODE0/NODE3 (the 4-node harness's two source slots), so on
+# the 8-node fleet agents on node5-7 would survive into the next N and collide.
+scale_down() {
+    local n
+    for n in "${SRC[@]}"; do stop_node "$n" >/dev/null 2>&1 || true; done
+    stop_node "${COLD_HOST}" >/dev/null 2>&1 || true
+    stop_node "${WARM_HOST}" >/dev/null 2>&1 || true
+    backend_down >/dev/null 2>&1 || true
+}
+
+ARM=asap
+slog "RUN_ID=${RUN_ID} N_LIST='${N_LIST}' SOAK=${SOAK}s SRC='${SRC[*]}' card=${PER_AGENT_CARDINALITY} freq=${OTELAPP_FREQ_HZ} prod/agent=${N_PRODUCERS_PER_NODE}"
+
+for N in ${N_LIST}; do
+    [ "${N}" -gt "${#SRC[@]}" ] && { slog "N=${N} exceeds ${#SRC[@]} source nodes — skipping (use simulator)"; continue; }
+    slog "=== N=${N} ==="
+    scale_down; sleep 3
+    backend_up "${ARM}" >>"${OUT}/scale.log" 2>&1; sleep 6
+    active=(); for k in $(seq 0 $((N-1))); do launch_agent "${SRC[$k]}" "$((k+1))"; active+=("${SRC[$k]}"); done
+    slog "warmup ${WARMUP}s"; sleep "${WARMUP}"
+
+    rx0=$(nic_rx "${WARM_HOST}"); t0=$(date +%s.%N)
+    sleep "${SOAK}"
+    rx1=$(nic_rx "${WARM_HOST}"); t1=$(date +%s.%N)
+    stats=$(agent_stats "${active[@]}")
+    rxps=$(awk -v a="$rx0" -v b="$rx1" -v t0="$t0" -v t1="$t1" 'BEGIN{printf "%.3f",(b-a)/((t1-t0)*1e6)}')
+    peragent=$(awk -v r="$rxps" -v n="$N" 'BEGIN{printf "%.3f", r/n}')
+    echo "${N},${ARM},${rxps},${peragent},${stats}" >> "${CSV}"
+    slog "N=${N}: sink_rx=${rxps} MB/s  per_agent=${peragent} MB/s  agent(cpu%,mem MiB)=${stats}"
+    scale_down
+done
+
+slog "done -> ${CSV}"; cat "${CSV}"
diff --git a/deploy/mvp-multinode/scripts/snapshot_resources.sh b/deploy/mvp-multinode/scripts/snapshot_resources.sh
index f0df56936..7c0b553a3 100755
--- a/deploy/mvp-multinode/scripts/snapshot_resources.sh
+++ b/deploy/mvp-multinode/scripts/snapshot_resources.sh
@@ -10,14 +10,21 @@ DUR=${2:-60}
 OUT=${3:-/mydata/mvp-multinode/results/snap}
 mkdir -p "$OUT"
 
-NODES=(node0 node1 node2 node3)
+# Node set is configurable so the same snapshotter works for the 4-node MVP
+# and the 8-node scaling cluster. Defaults to the original 4 nodes.
+read -ra NODES <<< "${SNAP_NODES:-node0 node1 node2 node3}"
+
+# Resolve the experiment-LAN NIC by IP subnet instead of a hard-coded name —
+# CloudLab hardware varies (enp130s0f0 on the original 4-node profile, eno2 on
+# the 8-node profile). Mirrors the auto-detect already in measure_nic_bw.sh.
+IFACE_PROBE='iface=$(ip -4 -o addr show | awk '"'"'$4 ~ /^10\.10\.1\./ {print $2; exit}'"'"'); iface=${iface:-eth0}'
 
 echo "[snap] arm=${ARM} duration=${DUR}s out=${OUT}"
 
 # 1. Capture START NIC bytes on each node and START container stats.
 declare -A start_rx start_tx start_t end_rx end_tx end_t
 for n in "${NODES[@]}"; do
-  start=$(ssh "$n" 'iface=enp130s0f0; echo "$(cat /sys/class/net/$iface/statistics/rx_bytes) $(cat /sys/class/net/$iface/statistics/tx_bytes) $(date +%s.%N)"')
+  start=$(ssh "$n" "${IFACE_PROBE}"'; echo "$(cat /sys/class/net/$iface/statistics/rx_bytes) $(cat /sys/class/net/$iface/statistics/tx_bytes) $(date +%s.%N)"')
   start_rx[$n]=$(echo "$start" | awk '{print $1}')
   start_tx[$n]=$(echo "$start" | awk '{print $2}')
   start_t[$n]=$(echo "$start" | awk '{print $3}')
@@ -58,7 +65,7 @@ wait
 
 # 3. Capture END NIC bytes on each node.
 for n in "${NODES[@]}"; do
-  ev=$(ssh "$n" 'iface=enp130s0f0; echo "$(cat /sys/class/net/$iface/statistics/rx_bytes) $(cat /sys/class/net/$iface/statistics/tx_bytes) $(date +%s.%N)"')
+  ev=$(ssh "$n" "${IFACE_PROBE}"'; echo "$(cat /sys/class/net/$iface/statistics/rx_bytes) $(cat /sys/class/net/$iface/statistics/tx_bytes) $(date +%s.%N)"')
   end_rx[$n]=$(echo "$ev" | awk '{print $1}')
   end_tx[$n]=$(echo "$ev" | awk '{print $2}')
   end_t[$n]=$(echo "$ev" | awk '{print $3}')
diff --git a/deploy/mvp-multinode/scripts/soak_rss.sh b/deploy/mvp-multinode/scripts/soak_rss.sh
new file mode 100644
index 000000000..a0523eb9f
--- /dev/null
+++ b/deploy/mvp-multinode/scripts/soak_rss.sh
@@ -0,0 +1,61 @@
+#!/usr/bin/env bash
+# soak_rss.sh — Fig 6b: bring up the asap arm and sample agent RSS over a soak
+# window to check for a memory leak (slope of RSS vs time). A 24h soak is the
+# paper target; this runs SOAK_MIN minutes (default 30) as the in-session proxy
+# and reports the MiB/hour slope (extrapolated to 24h).
+#
+# Usage: TOPOLOGY_ENV=... SKIP_BUILD=1 SKIP_LOAD=1 soak_rss.sh [SOAK_MIN]
+set -uo pipefail
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+TOPOLOGY_ENV="${TOPOLOGY_ENV:-${SCRIPT_DIR}/../topology.8node.env}"
+source "${TOPOLOGY_ENV}"
+SOAK_MIN="${1:-30}"
+RUN_ID="${RUN_ID:-soak-$(date +%H%M%S)}"
+OUT="${RUN_BASE}/${RUN_ID}"; mkdir -p "${OUT}"
+CSV="${OUT}/soak_rss.csv"; echo "elapsed_s,node,container,rss_mib,cpu_perc" > "${CSV}"
+log(){ printf '[%s] [soak] %s\n' "$(date +%H:%M:%S)" "$*" | tee -a "${OUT}/soak.log"; }
+
+# bring up the asap arm (reuses run_demo.sh up)
+log "bringing up asap arm for ${SOAK_MIN}min soak"
+SKIP_BUILD=1 SKIP_LOAD=1 bash "${SCRIPT_DIR}/run_demo.sh" up asap >>"${OUT}/soak.log" 2>&1
+
+# the two data-source hosts the asap arm runs agents on (role-based; these are
+# the harness's two NODE0/NODE3 source slots = the first two SRC_HOSTS).
+read -r _SRCA _SRCB _rest <<< "${SRC_HOSTS:-${NODE0_HOST:-node3} ${NODE3_HOST:-node4}}"
+AGENT_NODES=("${_SRCA}" "${_SRCB}")
+start=$(date +%s); end=$((start + SOAK_MIN*60))
+log "sampling RSS every 30s until +${SOAK_MIN}min"
+while [ "$(date +%s)" -lt "${end}" ]; do
+    el=$(( $(date +%s) - start ))
+    for n in "${AGENT_NODES[@]}"; do
+        ssh -o BatchMode=yes "$n" "docker stats --no-stream --format '{{.Name}}|{{.MemUsage}}|{{.CPUPerc}}' 2>/dev/null | grep '^asap-agent'" 2>/dev/null \
+        | while IFS='|' read -r name mem cpu; do
+            mib=$(echo "$mem" | awk -F'/' '{print $1}' | awk '{v=$1; sub(/[A-Za-z]+/,"",v); u=$1; sub(/^[0-9.]+/,"",u); m=1; if(u=="GiB")m=1024; else if(u=="KiB")m=1/1024; printf "%.1f", v*m}')
+            echo "${el},${n},${name},${mib},$(echo "$cpu" | tr -d '%')" >> "${CSV}"
+        done
+    done
+    sleep 30
+done
+
+log "soak done; computing RSS slope"
+python3 - "${CSV}" <<'PY'
+import csv, sys
+from collections import defaultdict
+rows=defaultdict(list)
+for r in csv.DictReader(open(sys.argv[1])):
+    try: rows[(r['node'],r['container'])].append((float(r['elapsed_s']), float(r['rss_mib'])))
+    except: pass
+print("container,n,first_mib,last_mib,slope_mib_per_hr,proj_24h_mib")
+for k,v in sorted(rows.items()):
+    if len(v)<3: continue
+    v.sort()
+    n=len(v); xs=[p[0] for p in v]; ys=[p[1] for p in v]
+    mx=sum(xs)/n; my=sum(ys)/n
+    den=sum((x-mx)**2 for x in xs) or 1
+    slope=sum((x-mx)*(y-my) for x,y in zip(xs,ys))/den  # MiB/sec
+    sph=slope*3600
+    print(f"{k[1]},{n},{ys[0]:.0f},{ys[-1]:.0f},{sph:.2f},{ys[-1]+sph*24:.0f}")
+PY
+
+SKIP_BUILD=1 SKIP_LOAD=1 bash "${SCRIPT_DIR}/run_demo.sh" down >>"${OUT}/soak.log" 2>&1
+log "torn down -> ${CSV}"
diff --git a/deploy/mvp-multinode/topology.8node.env b/deploy/mvp-multinode/topology.8node.env
new file mode 100644
index 000000000..6123edf69
--- /dev/null
+++ b/deploy/mvp-multinode/topology.8node.env
@@ -0,0 +1,166 @@
+# Multi-node MVP topology — 8-node CloudLab cluster on 10.10.1.0/24, 10Gbps.
+# Sourced by run_demo.sh / run_demo_sweep.sh via TOPOLOGY_ENV override.
+#
+# Role split for the 8-node eval cluster:
+#   node0 (10.10.1.1)  DRIVER / orchestrator (runs the harness, no containers)
+#   node1 (10.10.1.2)  COLD backend  -> harness NODE1
+#   node2 (10.10.1.3)  WARM backend  -> harness NODE2
+#   node3 (10.10.1.4)  data source A -> harness NODE0 (agent-a)
+#   node4 (10.10.1.5)  data source B -> harness NODE3 (agent-b)
+#   node5-7            extra source capacity, used by the scaling driver (SRC_NODES)
+#
+# Machines are named by ROLE (not an opaque NODEk index). USE THESE in new
+# scripts; the NODE0..NODE3 below are derived compatibility shims for the team's
+# run_demo.sh (which still speaks its 4-slot model).
+
+# ── Role-based names (the source of truth) ──
+DRIVER_HOST="${DRIVER_HOST:-node0}"; DRIVER_IP="${DRIVER_IP:-10.10.1.1}"  # orchestrator, no containers
+COLD_HOST="${COLD_HOST:-node1}";     COLD_IP="${COLD_IP:-10.10.1.2}"      # MinIO, Thanos {query,store,compact}, gorilla-merger, Prometheus, VictoriaMetrics
+WARM_HOST="${WARM_HOST:-node2}";     WARM_IP="${WARM_IP:-10.10.1.3}"      # data_plane + control_plane
+SRC_HOSTS="${SRC_HOSTS:-node3 node4 node5 node6 node7}"                    # data sources (agents + producers)
+SRC_IPS="${SRC_IPS:-10.10.1.4 10.10.1.5 10.10.1.6 10.10.1.7 10.10.1.8}"
+
+# ── Compatibility shims for run_demo.sh's 4-slot model, DERIVED from the roles
+# above (one source of truth). Its NODE0 = data-source-A, NODE1 = cold,
+# NODE2 = warm, NODE3 = data-source-B.
+read -r _SRC_A _SRC_B _rest <<< "${SRC_HOSTS}"
+read -r _SRC_A_IP _SRC_B_IP _rest_ip <<< "${SRC_IPS}"
+NODE0_HOST="${NODE0_HOST:-${_SRC_A}}";    NODE0_IP="${NODE0_IP:-${_SRC_A_IP}}"   # data source A / agent-a
+NODE1_HOST="${NODE1_HOST:-${COLD_HOST}}"; NODE1_IP="${NODE1_IP:-${COLD_IP}}"     # COLD backend
+NODE2_HOST="${NODE2_HOST:-${WARM_HOST}}"; NODE2_IP="${NODE2_IP:-${WARM_IP}}"     # WARM backend
+NODE3_HOST="${NODE3_HOST:-${_SRC_B}}";    NODE3_IP="${NODE3_IP:-${_SRC_B_IP}}"   # data source B / agent-b
+
+# ── Scaling / coordinated-sampling drivers (role-based) ──
+SRC_NODES="${SRC_NODES:-${SRC_HOSTS}}"                            # full source pool node3..node7
+SNAP_NODES="${SNAP_NODES:-${COLD_HOST} ${WARM_HOST} ${SRC_HOSTS}}" # resource-snapshot set (backends + sources)
+
+# ── DNS aliases that get injected via --add-host on every container.
+# Preserves the existing service-name references in the agent/backend
+# YAML configs (e.g., data-plane:9091, data-plane:4317, control-plane:4320,
+# minio:9000, thanos-query:10903) without rewriting any of them.
+#
+# Issue #400 (2026-05): the `gateway:` alias was retired with the
+# asap-gateway service — agents now push OTLP straight to data-plane:4317.
+#
+# COLD/WARM SPLIT (2026-05): the cold/thanos backend (MinIO, Thanos
+# {query,store,compact}, gorilla-merger, Prometheus) was moved off node2
+# onto the previously-idle node1, so the WARM engine (data_plane +
+# control_plane) gets a dedicated host. The data_plane's unbounded
+# in-memory SketchStore had been able to exhaust the whole 251 GiB node;
+# isolating it (plus the per-container --memory caps in run_demo.sh) bounds
+# the blast radius. Because --add-host injects this WHOLE map into every
+# container regardless of node, only the IPs below change — every container
+# still resolves every alias. So: warm aliases → NODE2_IP, cold aliases →
+# NODE1_IP. The data_plane (node2) reaches thanos-query/minio (node1) and
+# the agents cold-ship to gorilla-merger (node1) purely via these aliases.
+ADD_HOSTS=(
+    # ── WARM backend ──
+    --add-host=control-plane:${WARM_IP}
+    --add-host=data-plane:${WARM_IP}
+    # ── COLD / thanos backend ──
+    --add-host=minio:${COLD_IP}
+    --add-host=prometheus:${COLD_IP}
+    --add-host=prometheus-b0:${COLD_IP}
+    --add-host=victoriametrics:${COLD_IP}
+    --add-host=thanos-query:${COLD_IP}
+    --add-host=thanos-store-gateway:${COLD_IP}
+    --add-host=thanos-compact:${COLD_IP}
+    --add-host=gorilla-merger:${COLD_IP}
+    # ── data sources (the two harness source slots) ──
+    --add-host=agent-a:${NODE0_IP}
+    --add-host=agent-b:${NODE3_IP}
+    # b3 (serf wire codec) serf-gateway also lives on the cold node. Its :9000
+    # would collide with MinIO:9000, but the b3 and asap arms never run together,
+    # so there's no concurrent conflict.
+    --add-host=serf-gw:${COLD_IP}
+)
+
+# ── Workload sizing for ≥1 GB/s baseline raw bandwidth.
+# 200K series total @ 100 Hz × ~50 B/OTLP sample ≈ 1 GB/s aggregate.
+# Split: 5 producers/node × 20K series × 100 Hz on node0 + node3.
+PER_AGENT_CARDINALITY="${PER_AGENT_CARDINALITY:-1000}"
+OTELAPP_FREQ_HZ="${OTELAPP_FREQ_HZ:-100}"
+# Raw-buffer (the patched OTel SDK extension): every generated event is kept as
+# its own DataPoint and emitted on the periodic-reader tick, so the agent sees
+# the full FREQ_HZ-per-series stream (NOT one cumulative roll-up per window).
+# This is what feeds the cold gorilla archive its dense, per-series raw samples
+# — the cold tier is the raw backup, so a window must carry ~FREQ_HZ×WINDOW
+# samples/series, not 1.
+#
+# `default` (cumulative Sum/LastValue) collapses all FREQ_HZ events in a window
+# into a SINGLE datapoint/series, starving the cold archive to ~1 sample/series.
+# Use it only for a stock-OTel comparison arm, and only with -sdk-window
+# small enough that each event lands in its own window.
+OTELAPP_SDK_WINDOW="${OTELAPP_SDK_WINDOW:-1s}"
+OTELAPP_SDK_AGG="${OTELAPP_SDK_AGG:-raw-buffer}"
+# Per-series event buffer cap. Must hold ≥ FREQ_HZ × WINDOW events/series so a
+# full window's raw stream survives without drops (100 Hz × 1 s = 100; 4096
+# leaves generous headroom for jitter / longer windows). Events beyond the cap
+# are dropped + counted, not aggregated.
+OTELAPP_MAX_BUFFER_PER_SERIES="${OTELAPP_MAX_BUFFER_PER_SERIES:-4096}"
+# -freshness-probes is a bool flag (true/false).
+OTELAPP_FRESHNESS_PROBES="${OTELAPP_FRESHNESS_PROBES:-true}"
+OTELAPP_FRESHNESS_PROBE_HZ="${OTELAPP_FRESHNESS_PROBE_HZ:-1.0}"
+# The deprecated rate knob was dropped (it was ignored under SDK aggregation).
+# The built-in five-sketch workload (5 sketch types × 5K series = 25K sketch
+# instances per producer) always runs — the controller now derives each
+# metric's storage tier (warm-only sketch vs cold archive) on the asap_edge
+# processor, so the producer just emits the metrics unconditionally. The
+# five-sketch on/off gate was removed; cardinality knobs
+# (-five-sketch-user-pool etc.) still apply.
+# 5 producers/node × 1K cardinality at 100Hz raw-buffer → ~250 MB/s aggregate
+# raw event rate, ~50 MB/s aggregate wire. Higher producer counts crash the
+# docker daemon on the producer nodes (each raw-buffer producer is
+# CPU+RAM hungry at this freq).
+N_PRODUCERS_PER_NODE="${N_PRODUCERS_PER_NODE:-5}"
+
+# ── Soak / replay timing.
+# WARMUP_S=60 (was 30) gives the agent's first 30s tumbling window time to
+# close and its sketch state to be flushed to the backend before replay
+# starts. With WARMUP_S=30 the first ~14 seconds of soak fired before any
+# window had closed → backend had no sketch state → asap-arm queries with
+# `quantile_over_time(...[5m])` / `sum_over_time(...[5m])` / instant
+# `sum by (zone) (http_requests_total)` returned 14 leading "error/200"s.
+# `rate(...[5m])` queries survived only because the 5-min lookback eventually
+# found a closed window. 60s = single tumble + headroom; bump to 90 if a
+# future run still shows >3 leading failures on the same queries.
+WARMUP_S="${WARMUP_S:-60}"
+SOAK_S="${SOAK_S:-90}"
+
+# ── Image set (must be loaded on every node).
+#
+# data_plane reorg (2026-05): the control plane and data plane now ship
+# as TWO separate images built from ASAPQuery-backend's per-crate
+# Dockerfiles — `asap/data-plane:dev` (data_plane / query backend,
+# entrypoint `/usr/local/bin/data_plane`) and `asap/control-plane:dev`
+# (control_plane / controller, entrypoint `/usr/local/bin/control_plane`).
+# This retires the old combined query-backend image (built by
+# the now-deleted deploy/docker/Dockerfile.backend). The two binaries run
+# as separate processes — `run_demo.sh::backend_up()` brings up an
+# `asap-control-plane` container alongside the `asap-data-plane` container
+# on node2, mirroring the singlenode compose stack's two services.
+IMAGES=(
+    asap/asap-otel:dev
+    asap/otel-app:dev
+    asap/data-plane:dev
+    asap/control-plane:dev
+    # Thanos-Receive-style merger: ingests Gorilla XOR fragments over HTTP,
+    # serves the <2h pending window over a Thanos StoreAPI, ships 2h blocks
+    # to the asap-gorilla-tsdb bucket. Built by /mydata/asap-build-all.sh.
+    asap/gorilla-merger:dev
+)
+EXTERNAL_IMAGES=(
+    minio/minio:latest
+    minio/mc:latest
+    prom/prometheus:v2.55.0
+    quay.io/thanos/thanos:v0.41.0
+)
+
+# ── Paths (assume /mydata on every node).
+ROOT=/mydata/ASAPCollector
+BACKEND=/mydata/ASAPQuery-backend
+SKETCHLIB=/mydata/asap_sketchlib
+DEPLOY=/mydata/mvp-multinode
+CONFIG_SRC=${ROOT}/deploy/mvp-multinode/configs
+RUN_BASE=${DEPLOY}/results
+LOG_BASE=${DEPLOY}/logs
diff --git a/docs/continuous-monitoring-aggregation-taxonomy.md b/docs/continuous-monitoring-aggregation-taxonomy.md
index aaef4b0f3..78b344e4b 100644
--- a/docs/continuous-monitoring-aggregation-taxonomy.md
+++ b/docs/continuous-monitoring-aggregation-taxonomy.md
@@ -1,5 +1,8 @@
 # Continuous Monitoring — Aggregation Taxonomy & the `[t1,t2]` Window Question
 
+> **Part of the CDM/GOS theory set.** The canonical, unified derivations live in [`sampling-cdm-gos-derivations.md`](sampling-cdm-gos-derivations.md) — start there. This doc is retained for the full per-series×window / series×timestamp / series×window taxonomy and the (agg_id, group_key) monitor-keying rule, which the canonical doc does not reproduce.
+
+
 > Design note. Companion to
 > [continuous-monitoring-tumbling-cost-analysis.md](continuous-monitoring-tumbling-cost-analysis.md)
 > (the per-family cost theory) and
diff --git a/docs/continuous-monitoring-tumbling-cost-analysis.md b/docs/continuous-monitoring-tumbling-cost-analysis.md
index 5fcb8c1f7..b99e69b7a 100644
--- a/docs/continuous-monitoring-tumbling-cost-analysis.md
+++ b/docs/continuous-monitoring-tumbling-cost-analysis.md
@@ -1,5 +1,8 @@
 # Continuous Monitoring over Tumbling Windows — Cost Analysis of the Existing Sketch Families
 
+> **Part of the CDM/GOS theory set.** The canonical, unified derivations live in [`sampling-cdm-gos-derivations.md`](sampling-cdm-gos-derivations.md) — start there. This doc is retained for its per-family (Sum/CMS/CS/DDSketch/KLL/HLL) tumbling-window cost derivations under the two disciplines, which the canonical doc does not reproduce.
+
+
 > **Design-doc / paper section** — theory only. For each sketch family we
 > already ship (Sum, DDSketch, KLL, Count-Min, Count-Sketch, HyperLogLog),
 > derive, under the **continuous distributed monitoring (CDM)** model
diff --git a/docs/delta-transmission-design.md b/docs/delta-transmission-design.md
index 71c68f407..3ef8ad84a 100644
--- a/docs/delta-transmission-design.md
+++ b/docs/delta-transmission-design.md
@@ -607,6 +607,11 @@ skip unknown encodings or treat them as full sketches.
 deltas. This is the mirror of Phase 3.
 
 **New processors:** `countminsketchmergeprocessor` / `countsketchmergeprocessor`
+> **Removed.** These Go merge processors implemented the gateway-side sketch
+> accumulator. The asap-gateway hop was retired (#400) and cross-edge merge now
+> happens on the backend `data_plane` (Rust), so the Go merge processors were
+> deleted (no pipeline ever wired them). This section is kept as the design of
+> record for the delta-reconstruction mechanism, now realized backend-side.
 
 **Per-partition accumulator state:**
 ```go
diff --git a/docs/design-gos-unified-edge-telemetry.md b/docs/design-gos-unified-edge-telemetry.md
new file mode 100644
index 000000000..94783fddf
--- /dev/null
+++ b/docs/design-gos-unified-edge-telemetry.md
@@ -0,0 +1,732 @@
+# GOS: A Unified Error / Threshold / Cost Framework for Distributed Edge Telemetry
+
+**One line.** One framework that unifies error-bounded sketching, coordinated
+sampling, Geometric Monitoring (GM/AutoMon), and OctoSketch-style change
+transmission into a single per-cell decision — reducing to **one atomic quantity**
+`e_j = k·T_j` (per-cell backend error) — with a unified relative error bound, a
+tunable memory/compute/communication objective, and a closed-form
+water-filling solution whose worst-case communication matches the
+Woodruff–Zhang lower bound `Θ̃(k/ε²)`.
+
+We call the construction **GOS** (Geometric-OctoSketch).
+
+---
+
+## 1. Context and requirements
+
+Distributed data collection and transmission with a centralized analytics
+backend. Edge telemetry must be **memory-, computation-, and
+communication-efficient** under cloud economics, while the backend serves
+**continuous, accurate, fresh** queries.
+
+- **Memory efficiency.** Cloud memory instances are priced above CPU instances.
+  With high-cardinality, high-frequency time-series metrics from many
+  distributed services, allocating *one sketch per series per window* puts heavy
+  memory pressure on the data-source nodes. Memory must be controlled across the
+  series×window space.
+- **Computation efficiency.** Per-sample sketch update work at the edge must stay
+  within line-rate CPU budgets on shared instances.
+- **Communication efficiency.** Cross-AZ egress is billed per byte; transmission
+  must be minimized.
+- **Continuous & accurate queries.** Monitoring queries are *long-running* over
+  the stream, not the database "on-demand" model. We care about the
+  **freshness/timeliness** gap between when a sample is generated at the source
+  and when it can be queried accurately at the backend.
+
+These four axes are exactly the decision variables and constraints of the
+optimization below.
+
+---
+
+## 2. Positioning: what each prior line gives, and what it lacks
+
+All four lines are instances of "keep the local drift inside a **safe zone**,
+communicate on violation." They differ in the *shape* of the safe zone and in
+*what* they bound. (Attribution matters; see §10.)
+
+| Line | Bounds | Safe-zone shape | Continuous query? | Gap |
+|---|---|---|---|---|
+| **Error-bounded sketching** (Count-Sketch [Charikar+02], CMY [Cormode+08]) | one function, per window | — | window granularity only | intra-window staleness |
+| **Geometric Monitoring** (Sharfman–Schuster–Keren [SIGMOD'06]; AutoMon [Sivan+ SIGMOD'22]) | one scalar `f(x̄)` | DC-quadric (ball / slab / general) | that **one function** only | not per-cell; not query-general |
+| **OctoSketch** [Zhang+ NSDI'24] | **every counter cell** `|ΔC[j]|<T` | axis-aligned **box** | whole sketch, **any query, any time** | function-agnostic (uniform T) |
+| **Coordinated sampling** (NitroSketch; ASAP `AllocateSampleRates`) | edge CPU / counts | per-site rate `p_i` | — | ingestion-side only |
+| **Lower bound** (Woodruff–Zhang [STOC'12]) | `F_p` continuous, `(1±ε)` | — (yardstick) | — | it is a *limit*, not a protocol |
+
+Key facts used below:
+- **GM's scalar safe zone is a special case of the box** (OctoSketch box = GM with
+  an `L∞` safe zone), and **linear functions (sum/count/point) = GM with `λ=0` →
+  slab**, **`F_2` = GM with constant Hessian `2I` → ball**. So *counter-based* and
+  *`F_2`* monitoring are the same monitor at different Hessians (§7).
+- **OctoSketch gives the "whole sketch continuously queryable" property that GM
+  does not** — because it bounds every cell, not one function.
+- **GOS = adopt water-filling (classic; already used in ASAP for sampling) to
+  allocate *per-cell* thresholds, weighted by GM/AutoMon's gradient `∇f`, capped
+  by OctoSketch's query bound.** The optimization tool is off-the-shelf; the
+  contribution is the *combination*.
+
+---
+
+## 3. Model and decision variables
+
+`k` edge sites, one backend/coordinator. Site `i` observes a substream with true
+frequency vector `f_i`; the global signal is `f = Σ_i f_i`. Each site maintains a
+**linear sketch** `x_i = L f_i ∈ ℝⁿ`, `n = d·w` (Count-Sketch: `d` rows of ±1
+hashing, `w` buckets). Sketches merge additively: ideal global `C = Σ_i x_i = L f`.
+
+**Workload.** A set `Q` of linear queries `q(f)=⟨a_q,f⟩` (point, sum,
+heavy-hitter), each answered by a sketch estimator `q̂=⟨r_q,C⟩`; plus monitored
+non-linear functionals `f_m(f)` (e.g. `F₂=‖f‖₂²`, entropy, ratios) answered by
+`f̂_m = F_m(C)`.
+
+**Decision variables** (per metric group):
+
+| Variable | Meaning | Trades |
+|---|---|---|
+| `(d, w)` | sketch shape | accuracy ↔ memory |
+| `G` | # sketch instances (grouping over series) | accuracy ↔ **memory** |
+| `p_{i,r} ∈ (0,1]` | per-**row** admission rate (SDK-side) | edge **CPU** ↔ accuracy |
+| `T_j ≥ 0` | per-cell transmission threshold | **communication**/freshness ↔ accuracy |
+| flags | delta-vs-full, geometric-refs, uniform-vs-aniso | comm ↔ edge/coord memory |
+
+### 3.1 The SDK↔collector sampling split (row-admission)
+
+The sampling knob is **per-row admission decided at the SDK**, not a whole-sketch
+Bernoulli at the collector — and, crucially, **the SDK does not hash**. A
+Count-Sketch/CMS update touches `d` rows, one counter per row; the natural
+sampled unit is therefore a *candidate row update* `u = (item x, row r)`, not the
+raw item. The pipeline is three stages with a clean responsibility split:
+
+```
+raw measurement (item x, value Δ)
+  │
+  ├─[SDK]  row-admission by GEOMETRIC skip-sampling (NitroSketch), NOT a coin
+  │        per row. The SDK keeps a running skip-counter over the flattened
+  │        candidate stream (item,row); on admit it draws one gap
+  │        g ~ Geometric(p_{i,r}) and skips g candidates before the next admit.
+  │        admitted rows  R(x) = { rows whose candidate index the counter lands on }.
+  │        if the gap overruns all d rows  →  DROP the sample in O(1)
+  │           (no per-row work, never sent, never deserialized).
+  │        else send  (x, Δ, R(x))  to the agent collector.
+  │
+  └─[collector]  for each admitted r ∈ R(x):  compute the column h_r(x) and sign
+           s_r(x), then  C[r][h_r(x)] += s_r(x)·Δ / p_{i,r}   (inverse-prob weight).
+```
+
+Three design points make this different from applying `p` at the collector:
+
+1. **Geometric skip-sampling, not per-candidate coin flips (NitroSketch).**
+   Flipping `d` coins per item costs `Θ(d·rate)` RNG. Instead the SDK draws a
+   single geometric gap per *admitted* update and decrements a skip-counter, so
+   the expensive RNG (`ln`, divide) is `O(1)` **amortized per admitted update** →
+   `Θ((Σ_r p_{i,r})·rate)` — the expensive part (RNG `ln`/divide) scales with
+   admits, not with the unsampled row count. The current implementation
+   (`UpdateStringSampledPerRow`) still steps the skip-counter once per row,
+   `Θ(d)` cheap integer decrements per item; the `O(1)` whole-item skip (draw the
+   gap, and if it exceeds the item's `d` rows skip the item without touching each
+   row) is an available optimization on the uniform-`p` flattened-stream variant,
+   not yet shipped. Either way the RNG is `∝ Σ_r p_{i,r}` ("always line rate" for
+   the costly draws). This is the existing `sketchlib-go/common` `GeometricSampler`.
+2. **The SDK decides *which rows*, not *which columns*.** It never computes a
+   hash. The hash (row → column) is the collector's job, done *only for admitted
+   rows*.
+3. **Only admitted samples cross the wire.** A sample that admits no row is
+   dropped at the source, so the collector **skips its deserialization and
+   hashing entirely** — the CPU/bandwidth saving compounds.
+
+**SDK configuration prerequisite.** To decide row/counter admission the SDK must
+know, *per series*, (a) **which sketch** that series feeds and (b) that sketch's
+**configuration** — the counter fan-out per item and its dimensions. The
+controller therefore pushes the `series → (sketch type, dims, {p_{i,r}})` mapping
+to the SDK over the same config channel that carries the collector's plan (so the
+two stay consistent: the SDK admits the exact rows the collector is prepared to
+hash).
+
+**Applicability — linear counter-array sketches only.** Update-sampling with
+`1/p` weighting is unbiased **iff the counter is additive** (inverse-probability
+weighting is a linear correction). So it applies to:
+
+| Sketch | Counter fan-out / item | Admission | Why |
+|---|---|---|---|
+| **CountMinSketch** | `d` counters (one per row) | per-**row** | additive counters |
+| **CountSketch** | `d` signed counters | per-**row** | additive (signed) counters |
+| **DDSketch** | 1 bucket | per-**item** (the `d=1` case) | additive bucket counts |
+| **KLL** | — | ✗ **not sampled** | non-linear *random compaction*; dropping/weighting an insert breaks the rank guarantee |
+| **HLL** | — | ✗ **not sampled** | idempotent register **MAX**; a dropped max is unrecoverable (`1/p` can't correct a max), biasing cardinality low |
+
+So the SDK runs row-admission for **DDSketch / CMS / CountSketch** and leaves
+**KLL / HLL** unsampled (they emit every update).
+
+**Implementation status (current vs this target).** Today's edge runtime
+implements a *weaker* form of this design, and closing the gap is tracked work:
+
+| | This design (target) | Current code |
+|---|---|---|
+| Algorithm | geometric skip-sampling | ✅ `sketchlib-go/common.GeometricSampler` |
+| Weighting | `1/p` on admit | ✅ `CountSketchWrapper.UpdateString` (`count /= sampleP`) |
+| Families | CMS/CS/DDSketch only | ✅ `applyGrantedSampleP` |
+| **Granularity** | per-**row** admission (subset of `d` rows; hash only if ≥1 admitted) | ✅ **per-row** — `CountSketch.UpdateStringSampledPerRow` (sketchlib), wired in both the collector wrapper and the SDK aggregator |
+| **Where** | SDK decides, then sends | ✅ **SDK-build path** — hosted in the OTLP SDK `CountSketch` aggregator (`opentelemetry-go-patch/.../aggregate/countsketch.go`, knob `sample_p`); collector-build path retains the wrapper fallback |
+
+The **granularity** matches the design (per-row admission, drop-before-hash,
+`1/p` weight — mirroring `asap_sketchlib/.../nitro.rs`), so the sampling term is
+the tight per-row estimator of §3.2 (median decorrelation, `ε_sa` in
+quadrature). The **location** is now realized on the **SDK-build path**: the
+OTLP metrics SDK's `CountSketch` aggregator hosts a per-series sampler and
+routes every measurement through `UpdateStringSampledPerRow`, so admission
+happens at the source. Two deployment modes and what each saves:
+
+- **SDK-build (sketch in the app).** The SDK builds the group sketch and ships
+  one matrix per window. Sampling here is the source-side realization of the
+  admission split: it cuts SDK hashing/update CPU (`∝ Σ_r p_{i,r}`) and keeps the
+  per-row estimator; the collector then deserializes **one sketch per series**
+  instead of the raw datapoint stream (the raw-datapoint deserialization saving
+  is intrinsic to sketch-in-SDK). Wire volume of the sketch matrix itself is
+  fixed (dense), so sampling's benefit here is CPU + estimator, not matrix bytes.
+- **Collector-build (raw datapoints to the edge).** The app SDK emits raw
+  `NumberDataPoint`s; the edge `otlpfilter` wire-thins **whole datapoints**
+  (per-metric geometric admission) *before* decode, so dropped datapoints are
+  never materialized — the bandwidth/deserialization saving lands at the edge
+  receiver. The per-row `1/p` weighting is then applied by the collector wrapper
+  on survivors. (Whole-item drop is the `R(x)=∅` fast path of the same geometric
+  sampler.)
+
+All three families are wired on the SDK-build path via a `sample_p` knob:
+**CountSketch** and **CountMinSketch** host a per-series sampler and route
+inserts through `UpdateStringSampledPerRow` / `InsertWithHashSampledPerRow`
+(per-row admission, `1/p` applied in-place, wire stays exact — no downstream
+rescale); **DDSketch** is the `d=1` whole-item case and uses the sketch's
+built-in `WithSampleP` (raw counts, wire stamps `p`, consumer rescales `×1/p`).
+`KLL`/`HLL` stay unsampled by design (§ applicability table).
+
+### 3.1.1 Single-location sampling via consistent (stateless) decisions
+
+The design has exactly **one** sampling stage per series — never SDK *and*
+collector compounding. Rather than relying on configuration alone to prevent
+double sampling, the admission decision itself is made **location-independent**:
+
+```
+admit(seed_series, occ, row) ⇔ U(seed_series, occ, row) < p        (pure function)
+```
+
+`U` is a splitmix64-derived uniform (`sketchlib-go/common.ConsistentAdmit`);
+`seed_series = FNV-1a(series key) ⊕ window-start` and `occ` is the item's
+**occurrence id**. Because the decision is a deterministic function of shared
+inputs — no RNG state — *any* pipeline stage evaluates it and gets the identical
+admitted-row set:
+
+- **SDK-build** (implemented): the SDK aggregator's `ConsistentSampler` owns
+  the decision (`occ` = per-series measurement counter, seed = FNV(attrs) ⊕
+  window start). Downstream sees only sketch frames; nothing to re-sample.
+- **Collector-build** (implemented): the raw datapoints cross the wire, and
+  the identity is wire-visible — **seed = `common.SeedForMetric(name)`**
+  (canonical FNV-1a-64 of the metric name) and **occ = `time_unix_nano / 1e6`
+  in milliseconds**, chosen because the decoded `Observation.TimestampMs` the
+  wrapper sees is ms-resolution, so both stages compute the *same integer*.
+  The wire-level `otlpfilter` and the collector wrapper are then **two views
+  of the same decision**:
+  - `otlpfilter.SampleState.SetParams(name → {P, Rows})` configures per-metric
+    `(p, d)`; the filter reads `time_unix_nano` (field 3, fixed64) from the
+    opaque datapoint bytes and drops it iff *no* row admits (`R(x)=∅`, the
+    `(1−p)^d` fast path) — never decoding attributes or values.
+  - the runtime threads `(obs.Metric, obs.TimestampMs)` to the sketch via the
+    optional `precompute.SampleIdentitySetter` interface (one cached assert
+    per series entry, in `recordLocked`); the wrappers re-evaluate the
+    identical per-row admissions on survivors and apply the weight once:
+    **CountSketch/CMS** per-row with in-place `1/p` (CMS's internal whole-item
+    sampler is switched off — the wire envelope stays exact, no downstream
+    rescale); **DDSketch** is the `d=1` case — admission moves out of the
+    sketch into the wrapper's consistent decision, and `SetWireSampleP` keeps
+    the raw-counts + envelope-`p` convention for the consumer's `×1/p`.
+
+  Re-evaluation is **idempotent** — recomputing a decision never dilutes
+  twice, because there is nothing stochastic left to re-draw. Contract tests
+  pin this: filter survivors ≡ stateless recomputation ≡ wrapper touches
+  (`otlpfilter.TestConsistentDecisionsMatchSurvivors`,
+  `sketches.TestCSWrapper_ConsistentAgreesWithFilter`,
+  `TestDDSketchWrapper_ConsistentD1`).
+
+**Timestamp edge cases.** A datapoint with absent/zero `time_unix_nano` is
+passed through by the filter (fail-open — no wire identity to decide on) and
+sampled by the wrapper's per-series occurrence-counter fallback instead —
+exactly once end-to-end either way. Datapoints of the same metric sharing one
+millisecond share decisions (same `(seed, occ)`): per-occurrence unbiasedness
+holds, but errors within a same-ms burst are correlated — acceptable, and the
+reason `occ` granularity is ms (exact cross-stage agreement) rather than ns.
+
+**Grant plumbing (implemented).** The coordinator's per-round sampling grant
+reaches the wire filter automatically: `Grant.SampleP` arrives on the monitor
+channel → `monitor.Engine.OnGrant` stores it and fires `SetSampleGrantHook`
+(accepted grants only — stale-epoch/unknown are dropped, mirroring
+`grantedSampleP`) → the `asap_edge` processor's hook (`warm_sketch.go`) does
+`otlpfilter.Default().Upsert(inputMetricName, {P: p, Rows: d})`, with `d` from
+the family config (`wireSampleRows`: CS/CMS matrix rows, DDSketch 1; Sum/KLL/
+HLL never installed — same gating as `applyGrantedSampleP`). `p≤0`/`p≥1`
+grants withdraw the entry (no thinning). The `asap_otlp` receiver reads the
+same process-wide `otlpfilter.Default()` state (its factory defaults to it),
+so a single-pipeline collector needs zero extra wiring. Note `AggID =
+FNV-1a-64(metric)` is the SAME function as `SeedForMetric` — the grant key and
+the sampling seed are one identity.
+
+Enforcement remains one plan-level bit (`sample_at: sdk | collector`; the
+unselected side sees `p=1`), but consistency no longer *depends* on it: a
+misconfigured extra evaluation reproduces the same admitted set instead of
+squaring the sampling rate. `occ` must vary per **occurrence** — never per key
+alone, or a key's admissions become all-or-nothing and the §3.2 per-row
+decorrelation collapses to whole-key sampling. The filter's `Rows` must equal
+the target sketch's row count (plan consistency). A mismatch is asymmetric:
+`Rows` too LARGE is safe (the filter keeps extra points the wrapper then skips
+itself — wasted wire bytes, no bias); `Rows` too SMALL over-drops (the filter
+discards points whose higher rows the wrapper would have admitted — mass the
+`1/p` weight cannot recover, biasing estimates low). When in doubt, round up.
+
+**Window-start seed salt.** The per-series seed is XOR-salted with the window
+start (refreshed each delta-temporality collect). Without it, a series
+recreated each window (occurrence counter rewound) would repeat the identical
+admission pattern every window — per-window unbiased, but with sampling errors
+correlated across windows instead of averaging out. (The collector-build wire
+identity gets this for free: `TimeUnixNano` never repeats across windows.)
+
+**Fractional counts on the wire.** Per-row `1/p` weighting makes cells and
+deltas non-integral, so the proto wire carries them losslessly: full frames
+switch to packed-float64 `counts_float` when any cell is fractional (integral
+matrices keep the compact sint64 wire, byte-identical to before), and sparse
+deltas ride the new `d_counts_float` field under the same rule. The SDK's
+`payloadFor` additionally falls back to a full frame on any delta error, so an
+export can never wedge a series. (The msgpack-heap delta wire keeps its i64
+contract with the Rust backend and is unaffected.)
+
+**Unbiasedness.** For an admitted row-update the collector applies weight
+`1/p_{i,r}`; since `E[Z_r · 1/p_{i,r}] = 1`, each row-`r` sub-sketch is an
+unbiased estimator of `f`. The Count-Sketch readout `median_r s_r(x)·C[r][h_r(x)]`
+keeps its `(ε_sk, δ)` guarantee; sampling only inflates the per-row variance.
+
+**Variance / `ε_sa`.** An admitted counter carries weight `1/p_{i,r}`, so its
+contribution to the row-`r` cell variance is `(1−p_{i,r})/p_{i,r}·Δ²`. Summed over
+a cell's traffic this is the sampling perturbation `Σ_r^{sa}` of §4; the
+median-of-`d` rows turns it into the effective relative sampling error `ε_sa`.
+
+**Costs (the whole point).**
+
+| | per raw item | scales with |
+|---|---|---|
+| **SDK CPU** | 1 geometric gap (RNG) per *admit* + `Θ(d)` cheap skip decrements per item | RNG `∝ (Σ_r p_{i,r})·rate`; bookkeeping `Θ(d·rate)` |
+| **Collector CPU** | `|R(x)| ≈ Σ_r p_{i,r}` hashes + updates | `(Σ_r p_{i,r}) · rate` (vs `d` unsampled) |
+| **Bandwidth / collector deserialization** | 0 if `R(x)=∅` | drop prob `∏_r(1−p_{i,r})` = `(1−p)^d` uniform |
+
+So `p_{i,r}` is a **joint edge-CPU + bandwidth** lever: lowering it cuts collector
+hashing (`Σ_r p_{i,r}`), cuts wire volume, and lets the SDK stay at line rate with
+only coin flips. The accuracy cost is the `ε_sa` term below; §7 allocates the
+sampling budget against it. **Today the rate is per-site** (`p_{i,r}=p_i`): the
+coordinator gives one `p_i` per site via the whole-sketch ε-floor
+`p_i = 1/(1+ε²·rate_i)` (see §7C — the per-key `√(f_i/rate_i)` water-filling is
+retired for sketch sampling). Per-row rate differentiation (a larger `p` for
+high-sensitivity rows) is a future refinement — the current benefit is the
+per-row *admission* decorrelation of §3.2, not per-row rate tuning.
+
+### 3.2 Error and threshold-allocation math under per-row SDK sampling
+
+**Per-row estimator.** With per-row admission, row `r`'s cell is
+`C[r][c] = Σ_{x:h_r(x)=c} s_r(x)·Δ_x·(Z_{x,r}/p_{i,r})`, `Z_{x,r} ~ Bernoulli(p_{i,r})`
+**independent across rows**. The row estimate `X_r = s_r(y)·C[r][h_r(y)]` is
+unbiased (`E[Z_{x,r}/p_{i,r}]=1`); the point estimate is `f̂(y)=median_r X_r`.
+
+**Per-row variance = collisions + sampling.**
+```
+Var[X_r] ≈  F₂/w                     (Count-Sketch hash collisions, F₂=‖f‖₂²)
+         +  (1−p_r)/p_r · S₂,r(y)    (sampling; S₂,r(y)=Σ Δ² routed through y's row-r cell)
+```
+
+**The decisive point — median decorrelation (why per-row ≠ per-item).** The
+Count-Sketch `(ε,δ)` guarantee comes from the **median over `d` rows**: if each
+row fails w.p. `≤ ⅓` *independently*, the median fails w.p. `2^{−Θ(d)} = δ`. That
+independence is exactly what the two schemes do or do not give:
+
+- **Per-row admission (target):** `Z_{x,r}` is independent across `r`, so the
+  `X_r` are independent → the median concentrates **both** the collision **and**
+  the sampling error → sampling error lands **inside** the `δ` guarantee.
+- **Whole-item admission (current):** one `Z_x` shared by every row. A key `y`'s
+  own contribution is `Δ_y·Z_y/p`, *identical in all rows*; if `y` is dropped
+  (`Z_y=0`) it is missing from **all** rows at once and the median cannot recover
+  it. The queried key's own contribution `Δ_y·(Z_y/p − 1)` (scale
+  `∝ f(y)·√((1−p)/p)`) is **identical in every row**, so this part is
+  **common-mode** — it **survives the median** and is **not** reduced by `d`.
+  (Cross-key collision noise carries independent row signs `s_r(·)` and does
+  partially decorrelate even here; the un-reducible piece is the key's own
+  term.) It must be bounded *before* the median and cannot be credited with the
+  `d`-fold amplification.
+
+> **Result.** At equal admitted work (`E[rows]=d·p` per item ⇒ same edge CPU),
+> per-row sampling keeps the sampling error inside the median's high-probability
+> envelope; whole-item sampling leaves it as an irreducible common-mode penalty.
+> Per-row is the **strictly better estimator** — this, not the relocation, is the
+> reason to push the decision into the SDK.
+
+**Effective `ε_sa` (composition).** Under per-row independence the sampling
+*variance* folds into the **same** `median_r` step as the collision variance —
+`Var[X_r] = F₂/w + (1−p)/p·S₂,r` under one median tail — so it composes **in
+quadrature** with `ε_sk`: `|f̂(y)−f(y)| ≤ √(ε_sk²+ε_sa²)·‖f‖₂` w.p. `1−δ`, with
+`ε_sa = Θ(√((1−p)/(p·w)))` (uniform `p`). This is exactly the random part of
+Theorem 1 (§4). Whole-item admission instead leaves a **common-mode** term that
+survives the median and adds to `ε_sk` **linearly** — strictly looser at equal
+edge cost.
+
+**Threshold-allocation coupling (row-dependent floor).** The delta gate `T_j` for
+cell `j=(r,c)` sits over a *row-`r`-subsampled* counter, so the
+"don't-transmit-finer-than-you-sample" floor of §7 is **row-indexed** by that
+row's admission rate:
+```
+T_j ≥ T_j^floor = √( V_j · (1−p_i)/p_i )      (rate is per-site p_i, admission per-row)
+```
+The GOS water-filling is unchanged in form; the ε-budget is split by §7 Layer B
+(staleness `ε_st` peeled linearly, then `ε_sk²+ε_sa² = (ε_q−ε_st)²`), and this
+floor closes the sampling↔threshold coupling.
+
+**Status.** The per-row *estimator* benefit above is **realized** in code via
+`UpdateStringSampledPerRow`, so `ε_sa` sits inside the `1−δ` median guarantee. The
+*location* move is realized on the **SDK-build path** — the OTLP SDK `CountSketch`
+aggregator hosts the sampler (§3.1) — so admission happens at the source; the
+collector-build path realizes the pre-deserialization saving at the edge
+`otlpfilter` (whole-datapoint wire-thinning). CMS/DDSketch SDK-build hosting is
+the remaining follow-up.
+
+---
+
+## 4. Unified error bound
+
+The backend holds `Ĉ(t)`, perturbed from the ideal `C(t)` by two **random**
+sources that the Count-Sketch median absorbs together, plus one **deterministic**
+staleness term that adds on top:
+
+1. **Sketch + sampling (random, one median).** Per row `r`, the cell estimate
+   carries hash-collision variance `≈ F₂/w` *and* per-row sampling variance
+   `≈ (1−p_i)/p_i · S₂,r` (§3.2 — the `1/p_i` weight is unbiased; per-row
+   admission keeps the rows independent). Because both live in the **same**
+   `median_r` step, they compose in quadrature into one high-probability tail:
+   `|q̂ − q(f)| ≤ √(ε_sk² + ε_sa²)·‖f‖₂` w.p. `1−δ`, with `ε_sk=Θ(1/√w)`,
+   `ε_sa=Θ(√((1−p)/(p·w)))`, `δ=2^{−Θ(d)}`. (Sampling rate is per-site `p_i`;
+   admission is per-row-independent, i.e. `p_{i,r}=p_i` uniform across rows.)
+2. **Staleness `{T_j}` (deterministic).** Site `i` withholds cell `j` until its
+   accumulated change reaches `T_j`, so `|Ĉ[j] − (C+η)[j]| ≤ k T_j =: e_j` **at
+   all times** — a worst-case bound, *not* a random variable, so it **adds
+   linearly** (it cannot be RMS-combined with the random tail).
+
+**Theorem 1 (unified relative error).** For any query `q` and any time `t`, w.p.
+`≥ 1 − δ`:
+
+```
+              ┌──── random (one median) ────┐   ┌──── staleness (linear) ────┐
+|q̂(t) − q(f)| ≤ √(ε_sk² + ε_sa²)·‖f‖₂        +   k·Σ_j |r_{q,j}|·T_j
+```
+
+Dividing by `‖f‖₂` gives the **relative** budget: the random part (sketch ⊕
+sampling in quadrature) plus the deterministic staleness, linearly:
+
+> **√(ε_sk² + ε_sa²) + ε_st ≤ ε_q.**
+
+For a monitored non-linear `f_m` (`g_m = ∇f_m`, Hessian spectral bound `λ_m` from
+AutoMon/ADCD), a Taylor + DC bound gives
+
+```
+|f̂_m − f_m| ≤ |g_m|ᵀ(ε_sk + ε_sa terms) + k·Σ_j|g_{m,j}|T_j + ½·λ_m·k²‖T‖₂²  ≤  ε_m·|f_m|.
+```
+
+This holds **continuously**, not only at window boundaries — the OctoSketch
+"online accuracy at any query time" property, here generalized to arbitrary
+queries and to gradient-weighted function monitoring.
+
+### Freshness
+
+Cell `j` (activity `V_j = Σ_i V_{ij}`) reaches `T_j` after time `T_j / V_j`, so its
+max staleness age is `Δ_j = T_j / V_j`. To guarantee query freshness `Δ*`:
+`T_j ≤ V_j Δ*`. (Quiet cells have large `Δ_j` even at small `T_j`, so freshness
+binds them → periodic heartbeat.)
+
+---
+
+## 5. Cost models (per unit time)
+
+```
+Memory_edge  = m·G·n·( 1 + 1{delta}[acked snapshot] + 1{aniso}[threshold vec] )
+Comp_sdk     = c_rng·(Σ_r p_{i,r})·rate     (geometric skip-sampling: O(1)/admit RNG)
+Comp_coll    = c_h·(Σ_r p_{i,r})·rate       (hash + update — only admitted rows)
+             + c_s·Σ_j V_j/T_j              (uploads       — thresholds cut this)
+Comm         = b·Σ_j V_j/T_j  (+ broadcast for geometric)
+             × (1 − ∏_r(1−p_{i,r}))         (samples with no admitted row aren't sent)
+Cost_coord   = m·n·(1 + k·1{geo})           (running merge + per-edge refs)
+             + c_a·Σ_j V_j/T_j              (incremental apply_delta)
+```
+
+The edge CPU is split across the two runtimes: the **SDK** pays only the
+geometric-sampler RNG (`Comp_sdk`, `O(1)` per admit, no hashing), and the **agent
+collector** pays the hashing/update (`Comp_coll`) *only for admitted rows* plus
+the delta uploads. Lowering `p_{i,r}` cuts SDK RNG, collector hashing, and wire
+volume together — one lever, three savings.
+
+**Structural insight.** `Comm`, the upload part of `Comp_edge`, and the apply part
+of `Cost_coord` are all `∝ Σ_j V_j/T_j` → they collapse into one effective weight
+`W = w_c·b + w_e·c_s + w_b·c_a`. Hence the *cost weights do not change the optimal
+threshold shape* — the accuracy constraint pins `T_j`; the weights instead select
+the **structural knobs** (uniform-vs-aniso, delta-vs-full, sampling `p`, per-edge
+refs), which is where the memory/compute tradeoffs live.
+
+---
+
+## 6. The optimization problem (P)
+
+```
+minimize   w_m·Memory_edge + w_e·Comp_edge + w_c·Comm + w_b·Cost_coord
+over       d, w, G, {p_i}, {T_j}, structural flags
+subject to
+  (query,    ∀ q ∈ Q)   √(ε_sk(w)² + ε_sa({p_i})²) + k·Σ_j|r_{q,j}|T_j/‖f‖  ≤  ε_q
+  (function, ∀ f_m)      k·Σ_j|g_{m,j}|T_j + ½·λ_m·k²‖T‖²                    ≤  ε_m·|f_m|
+  (freshness)            T_j ≤ V_j·Δ*
+  (confidence)           d ≥ log₂(1/δ)
+                         p_i ∈ (0,1],  T_j ≥ 0,  w ≥ 1,  G ≥ 1
+```
+
+`w_b` (coordinator) is the least-weighted axis by requirement.
+
+---
+
+## 7. Solution: hierarchical decomposition + two water-fillings
+
+**Layer A — outer (small enumeration).** `d = ⌈log₂(1/δ)⌉`; choose `w`
+(`ε_sk = c/√w`) and `G` (grouping) to trade the memory term `w_m·G·n` against the
+accuracy the sketch must supply.
+
+**Layer B — budget split (staleness peeled linearly first).** Because staleness
+is deterministic it comes off the top of `ε_q` **linearly** (Theorem 1), *then*
+the remaining random budget is split in **quadrature**:
+
+1. choose `ε_st ∈ [0, ε_q − ε_sk]` — the edge-CPU↔communication knob (larger
+   `ε_st` ⇒ looser thresholds ⇒ less comm, but a smaller random budget ⇒ tighter
+   sampling ⇒ more CPU); pick it by the 1-D convex tradeoff of `w_e·Comp` vs
+   `w_c·Comm` (unique — both monotone);
+2. the random budget after the linear peel is `ε_rand = ε_q − ε_st`;
+3. split it in quadrature: `ε_sa² = ε_rand² − ε_sk²` (sketch ⊕ sampling), which
+   requires `ε_sk ≤ ε_rand`.
+
+So the composition is `√(ε_sk² + ε_sa²) + ε_st = ε_q`, matching §4 — **not** a
+three-way quadrature. The code's `split_budget(ε_q, ε_sk, w_edge, w_comm)`
+implements exactly this linear peel (`ε_st = t·(ε_q−ε_sk)`,
+`ε_sa = √((ε_q−ε_st)²−ε_sk²)`, `t = w_comm/(w_edge+w_comm)`).
+
+**Layer C — two water-fillings (same KKT tool, two variables).**
+
+- **Sampling** (per-site). The per-key KKT water-filling
+  `p_i ∝ √(f_i/rate_i)` (binding `Σ_i f_i(1−p_i)/p_i ≤ V_sa(ε_sa)`) is the
+  general form, but it has been **retired for sketch sampling**: a sketch
+  point/L2 estimate's error is bounded by the sketch *norm*, not a single key's
+  `f(x)`, so the accuracy a per-edge `p_i` buys is only "keep this edge's L2
+  contribution within ε." The **implemented** allocation is therefore the
+  whole-sketch ε-floor
+  ```
+  p_i = 1 / (1 + ε²·rate_i),   clamped to (0,1]
+  ```
+  (`data_plane monitor::coordinator::allocate_p` → `epsilon_sample_floor`; see
+  derivations §5). The `√(f/rate)` split remains valid only when a key is
+  exact-counted *outside* the sketch — where sampling that one counter is
+  pointless anyway.
+- **Thresholds** (per-cell; GOS). Effective weight `W` (§5), per-cell price
+  `c_j = k|g_j|` (function) or `k|r_{q,j}|` (query — take the binding constraint),
+  budget `B` (relative: `B = ε_m‖Ĉ‖²` for F₂):
+
+  ```
+              ┌ water-filling ┐   ┌──── clamps ────┐
+  T_j = clamp( (B/Σ_ℓ√(c_ℓ V_ℓ))·√(V_j/c_j),  T_j^floor,  min(T_q, V_j·Δ*) )
+  ```
+
+  with **sampling-coupling floor** `T_j^floor = √( V_j(1−p)/p )` ("don't transmit
+  finer than you sample"), **query cap** `T_q = ε_q‖Ĉ‖/(k·s_q)`, **freshness cap**
+  `V_j·Δ*`. Clamped cells release budget → box water-filling redistributes (a few
+  iterations).
+
+**Reading.** `T_j ∝ √(V_j/|g_j|)`: high-activity cells get larger thresholds (don't
+chase high-frequency noise); cells the monitored function is sensitive to
+(`|g_j|` large) get smaller thresholds (report early); everything capped by the
+universal query cap and freshness.
+
+### Closed forms
+
+- **F₂, isotropic** (`g_j = 2Ĉ_j`, `λ=2`, uniform, relative): `T = ε‖Ĉ‖ / (2k√(dw))`
+  — adaptive: scales with the current norm.
+- **F₂ threshold-alert version** (monitor `F₂ ≥ τ`, one-sided band): `T = (1/k)√((1−ε)τ/w)`.
+  The whole-sketch `F₂` readout here is the **mean-of-rows** estimator
+  `F̂₂ = ‖C‖²/d` (each row's `‖C_r‖²` is an unbiased `F₂` estimate; averaging the
+  `d` independent rows reduces its variance by `1/d`) — *not* the median-of-rows
+  point-query estimator of §3.2. This is deliberate: the geometric safe-zone is a
+  **ball** `‖C‖ ≤ √(d(1−ε)τ) ⇔ ‖C‖²/d ≤ (1−ε)τ`, so edge silence and coordinator
+  alert test the identical functional (`all-sites-safe ⟺ F̂₂ < (1−ε)τ`). The two
+  estimators serve different readouts — median for individual-key location
+  (robust tail), mean for the aggregate energy the ball bounds (variance
+  reduction) — and must not be conflated.
+- **Linear `f` (sum/count/point)**: `λ = 0` → the curvature term vanishes → box
+  degenerates to a **slab** = the classic CMY slack countdown.
+
+### Unification of counter-based and F₂
+
+Both are `f(x̄)` vs a band `[L,U]`, with the safe zone from the DC bound; the only
+difference is the Hessian eigenvalue: `λ=0` → slab (counter-based), `λ=const·I` →
+ball (F₂), general → ADCD quadric. One monitor, one edge check
+`isLocallySafe(Δ, x₀, ∇f, λ, L, U)`; keep the scalar-countdown fast-path for the
+linear case (no reference-vector broadcast needed).
+
+### Threshold band vs tracking band
+
+- **Threshold/alert**: one-sided band `[−∞, τ]` → fire on crossing.
+- **Continuous ε-query**: moving band `[f(x₀)−ε, f(x₀)+ε]` → backend answers
+  `f(x̄)=f(x₀)±ε` at all times. Same monitor, different band — this is how
+  threshold monitoring and continuous approximate querying unify (Cormode–
+  Garofalakis continuous querying = GM with a tracking band).
+
+---
+
+## 8. Optimality vs the Woodruff–Zhang lower bound
+
+**Rate vs total.** `Σ_j V_j/T_j` is an upload *rate*; the WZ `Θ̃(k/ε²)` is the
+*total* communication to maintain one continuous `(1±ε)` `F₂` estimate. Compare
+them over a fixed horizon of bounded total change: with `w ∝ 1/ε²` (the necessary
+sketch width) each sketch is `Θ(1/ε²)` and `k` sites must each be represented, so
+the total is `Θ̃(k/ε²)` — **matching the WZ STOC'12 tight lower bound** (bits vs
+words absorbed in the `Θ̃`). The measured normalization in
+[`gos-eval-results.md`](gos-eval-results.md) §3 uses the "one-round" unit `k·S`
+for exactly this comparison. Consequences:
+
+- The `1/ε²` and the linear-in-`k` are **fundamental**; no protocol (GM, AutoMon,
+  OctoSketch, GOS) beats `k/ε²` adversarially. GOS's savings are **data-dependent**
+  (small `V_j/‖Ĉ‖` on stable streams).
+- Report GOS's measured bytes as a **fraction of `k/ε²`** — a stronger baseline
+  than comparing to naive centralization.
+- The relative-error caveat: relative bounds require the norm bounded below
+  (WZ tightness / OctoSketch `L1 > ε⁻¹k'τ`); relative error on a near-zero signal
+  is fundamentally not cheap.
+
+---
+
+## 9. How it meets the four requirements
+
+| Requirement | Mechanism |
+|---|---|
+| **Memory efficiency** | `(w,G)` + `w_m`: grouping `G` avoids one sketch per series; `w=Θ(1/ε²)` is the WZ-minimum; delta/aniso flags dropped under high `w_m` (no snapshot / threshold vector) |
+| **Computation efficiency** | `w_e`: sampling `p_i` (fewer updates) + threshold size (fewer uploads); both fold into one effective weight |
+| **Communication efficiency** | `w_c`: per-cell water-filling thresholds + geometric silence; `Θ̃(k/ε²)` worst case, far less on stable data |
+| **Continuous, accurate, fresh** | Theorem 1 bounds every query at **any** `t`, **relative**; freshness cap `T_j ≤ V_jΔ*` bounds staleness age; whole sketch queryable (OctoSketch), monitored `f_m` tighter (GM/AutoMon) |
+
+---
+
+## 10. What is adopted vs contributed (attribution)
+
+- **Water-filling** — classic (information theory / convex optimization; optimal
+  power allocation across parallel channels). Already used in ASAP for sampling
+  (`p_i ∝ √(f_i/rate_i)`). *Adopted, not contributed.*
+- **Per-cell change transmission with a threshold** — OctoSketch [Zhang+ NSDI'24].
+  *Adopted.*
+- **Function safe zone via DC decomposition of the Hessian (ADCD), gradient/
+  Hessian bounds** — AutoMon [Sivan+ SIGMOD'22]; Geometric Monitoring [Sharfman+
+  SIGMOD'06]. *Adopted.*
+- **Coordinated sampling + geometric skip-sampling** — NitroSketch [Liu+
+  SIGCOMM'19]; ASAP's own `AllocateSampleRates` (rate allocation) and
+  `sketchlib-go/common.GeometricSampler` (the `O(1)`-amortized skip sampler).
+  *Adopted.*
+- **Lower bound `Θ̃(k/ε²)`** — Woodruff–Zhang [STOC'12]. *Yardstick.*
+- **GOS (this doc)** — casts *per-cell threshold allocation* as water-filling with
+  **gradient-derived weights** (GM/AutoMon) and a **per-cell query cap**
+  (OctoSketch), coupled to **per-site sampling** through a shared ε-budget and a
+  granularity floor `T_j ≳ √(V_j(1−p)/p)`, inside one **tunable
+  memory/compute/communication objective**. *The synthesis is the contribution;
+  the optimization tools are off-the-shelf.*
+
+---
+
+## 11. Implementation notes (controller synthesizes, edge executes)
+
+Everything expensive is a **controller (backend) decision**; the edge only
+executes a fixed per-cell comparison.
+
+- **Controller** (offline, per registered metric/query): runs ADCD (AD → Hessian
+  eigenvalue bounds → `∇f, λ`), estimates `{V_j}` from the workload, solves (P)'s
+  layers A–C, emits `(d, w, G, {p_i}, scalar GOS knobs, flags)` via OpAMP. This
+  slots into the existing controller multi-objective
+  (`controller-optimization-problem.md` SP-6:
+  `min w_bw·bw + w_cpu·cpu + w_mem·mem + …`) — GOS thresholds are new decision
+  variables there. **Note:** the controller ships *scalars*
+  (`ε_delta`, sites, aniso flag), **not** the full per-cell vector `{T_j}`; the
+  edge reconstructs `{T_j}` locally from those scalars plus its live sketch state
+  (see §7C, `sketches/gos_threshold.go`), so the `O(d·w)` vector never crosses the
+  wire.
+- **Edge**: maintain sketch + acked snapshot; per flush, recompute `{T_j}` from
+  the pushed scalars + local `{V_j}`, upload cells with `|ΔC_j| ≥ T_j` as a sparse
+  delta; run one generic `isLocallySafe` for monitored functions. No AD, no
+  water-filling solve at the edge — only the closed-form threshold evaluation.
+- **Backend**: `apply_delta` into a running merge (`O(#delta cells)`), keeping the
+  global sketch continuously queryable within the Theorem-1 envelope, surfaced in
+  the `accuracy: ε=…` response annotation.
+
+**Ties to existing code:**
+- `ASAPQuery-backend/control_plane/src/epsilon_alloc.rs` — the ε-budget split.
+  Staleness is peeled **linearly** first (Theorem 1), then the remaining random
+  budget splits in quadrature: `√(ε_sk² + ε_sa²) + ε_st = ε_q` (see §7 Layer B).
+  This is **not** a three-way quadrature `ε² = ε_sk² + ε_sa² + ε_st²` — staleness
+  is deterministic and comes off the top linearly.
+- `ASAPQuery-backend/data_plane/src/monitor/sampling_alloc.rs`
+  (`epsilon_sample_floor`) — the live whole-sketch sampling floor. (The Go
+  `monitor/sampling_alloc.go` `AllocateSampleRates` is the *retired* per-key
+  water-filling, kept only as a reference impl with no production caller.)
+- `threshold_alloc` (`AllocateThresholds`, Go `sketches/gos_threshold.go` + Rust
+  `control_plane/src/threshold_alloc.rs`) — the per-cell threshold water-filling
+  (this doc's §7C).
+- `data_plane/src/monitor/f2_coord.rs`, `asap-precompute-go/monitor/f2engine.go`
+  — generalize the F₂-specific ball to the `(∇f, λ)` DC safe zone; use the relative
+  radius `ε‖Ĉ‖/(2k√(dw))`.
+- reuse `asap_sketchlib` `CountSketchDelta` + `compute_delta`/`apply_delta`
+  (byte-parity Go/Rust) for the sparse per-cell delta wire format.
+
+**Implementation status (as of this writing).** The pieces exist but the GOS
+threshold control loop is **not yet wired end-to-end**:
+- **Live today:** the scalar CDM loop (register → grant `(slack, sample_p)` →
+  countdown → report → alert) and the sampling grant path (`Grant.SampleP` →
+  `otlpfilter` Upsert + wrapper `WithSampleP`).
+- **Implemented but unreachable from a production config:** the control plane
+  *derives and emits* the scalar GOS knobs (`gos_delta_epsilon`, `gos_sites`,
+  `gos_anisotropic` in `emit/agent.rs`), and the edge *consumes*
+  `PrecomputeConfig.GosDeltaEpsilon` (`applyGosMode` → `gosThresholdMatrix`), but
+  **no collector processor parses those YAML keys into the config**, so
+  `applyGosMode` is a production no-op and the per-cell delta-gating path is
+  exercised only by tests/eval. The sampling↔threshold coupling floor
+  `T_j ≥ √(V_j(1−p)/p)` (§3.2) is likewise implemented but inert — its only
+  production-shaped caller hardcodes `SampleP=1`. Closing this last hop
+  (a knob parser + threading the granted `p` into `GosParams`) is tracked work.
+
+---
+
+## 12. Open problems / next steps
+
+1. **Anisotropic delta broadcast** — *partially done.* The **sparse-cell**
+   encoding of `ΔC_ref` on the coordinator→edge path is implemented and measured
+   (`CRefUpdate::Delta`; removes the `O(k)` broadcast amplification — see
+   gos-eval-results.md §2). Still **open:** the broadcast gate is currently
+   isotropic (ships every changed cell, `Δ ≠ 0`); giving it *anisotropic per-cell
+   thresholds* (the §7C water-filling, as already done on the edge→coordinator
+   upload path via `ComputeDeltaPerCell`) is the remaining work.
+2. **Relative-error under small norm** — heartbeat / additive floor when `‖Ĉ‖` is
+   small (WZ / OctoSketch fundamental limit).
+3. **Verified eigenvalue bounds** — AutoMon's numerical `λ` may miss the true
+   extreme → reserve an `ε_eig` slice of the budget or use interval bounds.
+4. **Empirical validation** — measure achieved communication as a fraction of the
+   WZ `k/ε²`, sweep `(w_m, w_e, w_c)` to trace the Pareto surface.
+
+---
+
+### References (attribution)
+
+- G. Cormode, S. Muthukrishnan, K. Yi. *Algorithms for Distributed Functional Monitoring.* SODA 2008 / ACM TALG 2011.
+- M. Charikar, K. Chen, M. Farach-Colton. *Finding Frequent Items in Data Streams* (Count-Sketch). ICALP 2002.
+- I. Sharfman, A. Schuster, D. Keren. *A Geometric Approach to Monitoring Threshold Functions over Distributed Data Streams.* SIGMOD 2006.
+- H. Sivan, M. Gabel, A. Schuster. *AutoMon: Automatic Distributed Monitoring for Arbitrary Multivariate Functions.* SIGMOD 2022.
+- Y. Zhang, P. Chen, Z. Liu. *OctoSketch: Enabling Real-Time, Continuous Network Monitoring over Multiple Cores.* NSDI 2024.
+- D. Woodruff, Q. Zhang. *Tight Bounds for Distributed Functional Monitoring.* STOC 2012 (arXiv:1112.5153).
+- Z. Liu, R. Ben-Basat, G. Einziger, Y. Kassner, V. Braverman, R. Friedman,
+  V. Sekar. *NitroSketch: Robust and General Sketch-Based Monitoring in Software
+  Switches.* SIGCOMM 2019. (Geometric skip-sampling: one RNG draw per admitted
+  update — `O(1)` amortized, "always line rate" — with inverse-probability
+  weighting; the SDK-side row-admission sampler here is this scheme.)
diff --git a/docs/distributed-nitrosketch-coordinated-sampling.md b/docs/distributed-nitrosketch-coordinated-sampling.md
index 38949a396..a5650c465 100644
--- a/docs/distributed-nitrosketch-coordinated-sampling.md
+++ b/docs/distributed-nitrosketch-coordinated-sampling.md
@@ -1,5 +1,8 @@
 # Distributed NitroSketch — coordinator-allocated update-sampling across edge collectors
 
+> **Part of the CDM/GOS theory set.** The canonical, unified derivations live in [`sampling-cdm-gos-derivations.md`](sampling-cdm-gos-derivations.md) — start there. This doc is retained for the SDK→collector split survival proof (unbiasedness + partition-invariant variance), per-family applicability, and the 2026-06 empirical validation, which the canonical doc does not reproduce.
+
+
 > Design note. Extends [NitroSketch](https://doi.org/10.1145/3341302.3342076)
 > (per-sketch update-sampling, single stream) to a multi-edge setting using the
 > coordinated-sampling idea from the
@@ -8,6 +11,16 @@
 > [continuous-monitoring-aggregation-taxonomy.md](continuous-monitoring-aggregation-taxonomy.md)
 > and [continuous-monitoring-tumbling-cost-analysis.md](continuous-monitoring-tumbling-cost-analysis.md).
 
+> ⚠️ **Allocation law updated.** This note derives the per-key KKT water-filling
+> `p_i ∝ √(f_i/rate_i)`. For **sketch** sampling that allocation has been
+> **retired**: a sketch point/L2 estimate's error is bounded by the sketch
+> *norm*, not a single key's `f(x)`, so the implemented coordinator allocation is
+> the whole-sketch ε-floor `p_i = 1/(1+ε²·rate_i)`
+> (`data_plane monitor::coordinator::allocate_p` → `epsilon_sample_floor`; see
+> [design-gos-unified-edge-telemetry.md](design-gos-unified-edge-telemetry.md)
+> §7C and derivations §5). The `√(f/rate)` form below is retained for its
+> derivation and applies only when a key is exact-counted *outside* the sketch.
+
 ## Context
 
 Each edge collector builds per-metric sketches over its local stream and ships
diff --git a/docs/design-asap-otap-rust-integration.md b/docs/dormant/design-asap-otap-rust-integration.md
similarity index 99%
rename from docs/design-asap-otap-rust-integration.md
rename to docs/dormant/design-asap-otap-rust-integration.md
index 05e5311c1..e060a254b 100644
--- a/docs/design-asap-otap-rust-integration.md
+++ b/docs/dormant/design-asap-otap-rust-integration.md
@@ -1,5 +1,8 @@
 # ASAP OTAP-Rust Integration — Design
 
+> **DORMANT.** This integration is not active on the current branch — the `otel-arrow` path/submodule is uninitialized and no build or eval arm exercises it. Kept for design reference; revisit before reactivating.
+
+
 _Status: **draft** — 2026-05-02. Doc-only; gates the OTAP-Rust
 adapter work that becomes Phase 5 of the edge-framework migration._
 
@@ -582,7 +585,7 @@ shipped in #241 / #242).
   — five-layer model, bandwidth invariant, Strategy A/B,
   per-platform encoding, `Adapter` / `ControlChannel` traits,
   R6 (OTAP pre-1.0), §7.4 (integration model).
-- [`docs/design-asap-telegraf-integration.md`](./design-asap-telegraf-integration.md)
+- [`design-asap-telegraf-integration.md`](./design-asap-telegraf-integration.md)
   — Phase-4 adapter design that this Phase-5 design mirrors
   structurally (two-layer split, unified-plugin shape, build
   pipeline).
diff --git a/docs/design-asap-telegraf-integration.md b/docs/dormant/design-asap-telegraf-integration.md
similarity index 99%
rename from docs/design-asap-telegraf-integration.md
rename to docs/dormant/design-asap-telegraf-integration.md
index 22db724aa..d90287ce2 100644
--- a/docs/design-asap-telegraf-integration.md
+++ b/docs/dormant/design-asap-telegraf-integration.md
@@ -1,5 +1,8 @@
 # ASAP Telegraf Integration — Design
 
+> **DORMANT.** This integration is not active on the current branch — the `telegraf` path/submodule is uninitialized and no build or eval arm exercises it. Kept for design reference; revisit before reactivating.
+
+
 _Status: **draft** — 2026-05-02. Doc-only; gates the Telegraf
 adapter work that becomes Phase 4 of the edge-framework migration._
 
diff --git a/docs/design-asap-vector-integration.md b/docs/dormant/design-asap-vector-integration.md
similarity index 98%
rename from docs/design-asap-vector-integration.md
rename to docs/dormant/design-asap-vector-integration.md
index 295b6e700..7ad76c751 100644
--- a/docs/design-asap-vector-integration.md
+++ b/docs/dormant/design-asap-vector-integration.md
@@ -1,5 +1,8 @@
 # ASAP Vector Integration — Design
 
+> **DORMANT.** This integration is not active on the current branch — the `vector` path/submodule is uninitialized and no build or eval arm exercises it. Kept for design reference; revisit before reactivating.
+
+
 _Status: **draft** — 2026-05-02. Doc-only; gates the Vector adapter
 work that becomes the Phase 5 sister of the OTAP-Rust adapter in
 the edge-framework migration._
@@ -582,11 +585,11 @@ already on `main` (Phase 5 step A — #248).
   R6 / R7, §7.2 (per-platform Strategy-B carriers — Vector row),
   §7.3 (per-platform integration — Vector
   `TaskTransform<EventArray>`), §7.4 (integration model).
-- [`docs/design-asap-otap-rust-integration.md`](./design-asap-otap-rust-integration.md)
+- [`design-asap-otap-rust-integration.md`](./design-asap-otap-rust-integration.md)
   — Phase-5 sister adapter design that this doc mirrors
   structurally (two-layer split, unified-plugin shape, build
   pipeline, cross-language parity reasoning).
-- [`docs/design-asap-telegraf-integration.md`](./design-asap-telegraf-integration.md)
+- [`design-asap-telegraf-integration.md`](./design-asap-telegraf-integration.md)
   — Phase-4 adapter design that this doc inherits the
   unified-plugin and patch-overlay shape from.
 - [`docs/adr/adr-0002-extract-precompute-runtime.md`](./adr/adr-0002-extract-precompute-runtime.md)
diff --git a/docs/evaluation-plan-figures.md b/docs/evaluation-plan-figures.md
index a8eafc21c..3838c49e7 100644
--- a/docs/evaluation-plan-figures.md
+++ b/docs/evaluation-plan-figures.md
@@ -28,13 +28,55 @@ cold-half, Fig 11), and **the controller allocates the partition** (which series
 sketch[type,`W`,`L`,`p`] vs cold-Gorilla) from the query set (Fig 12).
 
 The contribution stack, from system to evidence:
-1. sketch-across-the-lifecycle + the `(W,L,agg_type)` planner (the base system);
-2. **coordinated SDK sampling** (`p_i ∝ √(f_i/rate_i)`, ε-floored) — new cost axis;
+1. sketch-across-the-lifecycle + the **autonomous `(ε, queries) → {sketch, W, L, p, τ}` planner** (the controller — the key originality);
+2. **coordinated SDK sampling** — the unified whole-sketch ε-floor `p = 1/(1+ε²·rate)` (the per-key `√(f/rate)` allocation was retired) — new cost axis;
 3. **CDM** — ε-gated sub-window delta emission (open-window freshness) + slack-countdown alert;
 4. the **joint bound** `ε_sk + ε_s + ε_cdm` (proof) tying accuracy to cost.
 
 ---
 
+## Experiment matrix — claim × dataset × baseline (the comparative-rigor plan)
+
+The figures below are the *mechanism* demos. For a submission they must each run on
+**≥2 real-world datasets** against **real baselines**, with **multiple trials + 95% CIs**.
+This matrix is the contract; the per-figure sections carry the current numbers.
+
+### Real-world datasets (workload-credibility axis — NO synthetic in the headline)
+| dataset | domain | shape / regime | exercises | status |
+|---|---|---|---|---|
+| **Google-cluster-2019** (`cpu_rate`) | machine resource usage | millions of (machine,job,task); high cardinality; continuous gauge | quantile/sum sketches, cardinality regime | ✅ staged (`/tmp/gct-*`) |
+| **DEBS-2022** (last-trade) | financial tick stream | ~5k symbols; high rate; Zipf-skewed | frequency/heavy-hitter (CMS/CountSketch/Topk), skewed-rate sampling | ◐ downloading (`debs/data/`) |
+| **3rd real** (Alibaba-2022 / Azure-VM / node-exporter dump) | infra metrics | TBD | generality / scale | ◻ gap |
+
+### Baselines (comparative axis — the biggest current gap)
+| baseline | what it is | claim it stresses |
+|---|---|---|
+| **b0 raw-OTLP** | full samples, no aggregation | bw / CPU / mem floor |
+| **b0a/b0b/b1 raw+codec** | gzip / zstd / Snappy only | bw from compression *alone* (isolate the aggregation factor) |
+| **Prometheus+Thanos / VictoriaMetrics** | deployed TSDB remote-write | real-world reference point |
+| **NitroSketch / OmniSketch** | sampling-sketch prior art | sampling *accuracy* vs ASAP's ε-floor |
+| **Cormode-style CDM** | functional/threshold monitoring | delta-emission / freshness |
+| **ASAP ablations** | no-sampling · uniform-p · no-CDM-delta · single-tier (warm-only) · **static (non-autonomous) alloc** | the marginal value of each ASAP knob |
+
+### The matrix (✅ measured · ◐ partial · ◻ gap)
+| # | Claim | Experiment | Metric | gct-2019 | DEBS-2022 | 3rd | vs baselines | rigor (trials/CI) |
+|---|---|---|---|---|---|---|---|---|
+| C1 | Bandwidth reduction | sketch-vs-raw on a true family slice (`c1_wire.py`) | wire bytes agent→backend | ✅ **DDSketch 33.8×±0.3, HLL 65.9×±0.8** (real gct, n=3, 95% CI) | ◻ | ◻ | ✅ vs raw-OTLP; ◐ **vs raw+gzip = net 2.7×** (gzip 12.3×, offline); ◻ Prom/Nitro | ✅ 3 trials + CI |
+| C2 | Edge CPU | sketch processors vs raw-forward, per-node | cpu cores | ◐ Fig 6 | ◻ | ◻ | ◐ vs raw | ◻ |
+| C3 | Edge memory | RSS bounded over long soak (no leak) | RSS slope | ✅ edge bounded; ⚠ backend leak (Fig 6) | ◻ | ◻ | ◐ vs raw | ◐ 1 soak |
+| C4 | Query accuracy | all-6-family error inside ε-envelope vs ground truth | rel-err, %≤ε, top-K recall | ✅ **DDSketch p50 0.72%/p99 2.67%, HLL 0.33%** (2026-06-20, root-caused valid); KLL small-N◐; CMS/CS → gauge-mismatch, use DEBS | ◻ (heavy-hitter natural here) | ◻ | ◻ **vs Nitro/Omni** | ◻ **need N trials + CI** |
+| C5 | Query latency | warm-sketch vs cold-fallback PromQL replay | p50/p99 ms | ✅ Fig 7 (warm+cold) | ◻ | ◻ | ◐ vs VM/Thanos native | ◐ |
+| H | **Pareto headline** | total (edge+wire+backend+storage) cost vs accuracy, swept over `(W,L,agg,p,ε)` | cost↔acc frontier | ◐ Fig 1 | ◻ | ◻ | ◻ **vs raw+Prom on same frontier** | ◻ |
+| N1 | **Autonomous allocation quality** | `(ε,queries)`→plan vs oracle/hand-tuned/naive | plan match-rate, cost↔acc gap | ◐ Fig 12 (mechanism ✅ on cluster; quality ◻) | ◻ | ◻ | vs static-alloc, all-DDSketch, all-raw | ◻ |
+| N2 | Controller adaptivity | inject query/workload drift → re-plan | re-plan latency, post-shift acc | ◻ | ◻ | ◻ | — | ◻ |
+| X1 | Coordinated vs uniform p | whole-sketch ε-floor (observable R) vs fixed-p; per-edge on a fleet | F1/heavy-hitter err, insert-tput | live grants ✅ | ✅ real CMS/DEBS: **22× insert-tput, F1 err held at ε (0.050@ε=.05)**; fleet cold-edge **7×** better than fixed-p (Fig 9a; earlier 40–60× per-key claim RETRACTED, was circular) | ◻ | ✅ vs NitroSketch (same sampler; ε-floor derives p) | ✅ |
+| X2 | Two-tier coverage | fraction warm- vs cold-answerable over a real query set; per-tier acc/latency | coverage %, per-tier | ◐ Fig 11 | ◻ | ◻ | — | ◻ |
+| X3 | CDM delta savings | egress vs always-send, swept over τ | emits/window, bytes | ✅ Table 1 (gct) ; ✅ DEBS cross-check (structural-skew caveat) | ✅ | ◻ | vs Cormode-CDM | ◐ |
+
+**Headline gaps to close (priority order):** (1) **real baselines** — at minimum b0a/b0b + Prometheus/Thanos + one sampling-sketch (NitroSketch), on the same Pareto; (2) **statistical rigor** — ≥5 trials + 95% CI on every accuracy/cost number (current runs are single-shot and noisy); (3) **all-6-family accuracy** clean (only DDSketch is solid); (4) a **DEBS-2022 end-to-end** pass (downloading) as the 2nd real axis; (5) **autonomous-allocation quality vs oracle** (mechanism is validated, decision quality is not); (6) **scale** beyond the light workload.
+
+---
+
 ## Fig 1 — HEADLINE: accuracy-vs-cost Pareto, swept over `(W, L, agg, p, ε_cdm)`  ◐
 **Claim:** ASAP dominates raw; sampling + CDM extend the frontier.
 **Layout:** scatter, x = total cost (edge CPU + wire bytes/s, normalized to raw=1.0),
@@ -121,6 +163,80 @@ a *timing*, not reducer, cause; pinned + fixed via `run.py --wall-clock-anchor`)
 dispersion is small-N order-statistic variance (single-window collapse, ~93 pts/series),
 not a sketch defect.
 
+#### (c′) Re-run on current `main` (2026-06-19) — two regressions surfaced  ⚠
+Same harness (`run_perfamily.py`, real gct, 1000 series), now on `main` (post ε-floor
+unification + autonomous-allocation merges). Results **diverge from (c)** and flag two
+issues to fix before this figure is submission-ready:
+
+| family | kind | median rel-err | %≤ε | wire | vs (c) |
+|---|---|---|---|---|---|
+| **DDSketch** | p50 / p99 | 0.0072 / 0.0267 | 87% / 41% | **57.6 MB** | acc ≈ same; **wire 58× higher** |
+| **HLL** | cardinality | rel-err **0.0033** (996.7/1000) | ✅ | 57.0 MB | acc ≈ same ✅ |
+| **KLL** | p50 / p99 | **0.0597** / 0.0717 | 48% / 41% | 57.9 MB | **acc 85× worse** (was 0.0007) |
+| **Count-Min** | freq | f̂=143 / f_true=244 | **one-sided VIOLATED** | 67.4 MB | was "exact, one-sided OK" |
+| **CountSketch** | topk@10 | recall **0.0** (n_warm=0) | ✗ | 25.8 MB | unchanged-broken |
+
+**Both flags ROOT-CAUSED (2026-06-20) — NOT a code regression; the accuracy is valid:**
+1. **57 MB wire = un-sliced data, not lost SDK-aggregation.** The per-family slices
+   `/tmp/perfam-*.jsonl` are **byte-identical 210 MB files each containing ALL 8 metric
+   aliases** (`cpu_rate`, `_q_ddsketch`, `_q_kll`, `_topk_cs/cms`, `_card_hll`, `_freq_cms`,
+   `memory_usage`). Each arm's agent **does** SDK-sketch its one configured metric (accuracy
+   proves it), but **forwards the other 7 raw**, dominating the wire. → C4 accuracy is sound;
+   **C1 wire must be re-measured with genuinely family-sliced inputs** (filter the slice to
+   the single metric). A data-prep gap in `make_perfamily.py`, not a sketch/agent regression.
+2. **KLL median + CMS one-sided are operating-point/data-fitness, not bugs.** KLL is
+   rank-based and coarse at the **~93 pts/series** the wall-clock single-window collapse
+   leaves (DDSketch's relative-error buckets handle small-N better — that's the real
+   tradeoff, not a KLL defect). CMS "under-counts" because gct `cpu_rate` is a **gauge** with
+   no meaningful per-key *count* to over-estimate — the frequency families are mis-applied to
+   gauge data and belong on **DEBS** (below), where symbol-trade frequency is a true count.
+
+**Data-fitness finding (not a bug):** gct `cpu_rate` is a **gauge** — quantile (DDSketch)
++ cardinality (HLL) are the natural fit and behave well; the **frequency/heavy-hitter
+families (CMS, CountSketch, Topk) are mismatched to gauge data** (there is no meaningful
+"count of a cpu_rate value"). Those families are evaluated on **DEBS-2022** (symbol-trade
+frequency = the natural heavy-hitter workload), not gct.
+
+#### (c″) CLEAN C1 bandwidth — fixed harness, real gct, 3 trials + 95% CI  ✅
+`c1_wire.py` (2026-06-20) closes the (c′) flags: it slices the data to a TRUE single-family
+set `{cpu_rate Sum-anchor, memory_usage, ONE sketch metric}` and replays the *same* slice
+through both a sketch agent and a **raw-forward agent** (`agent-raw-coldoff.yaml`, no
+`asap_edge`) for an apples-to-apples wire comparison.
+
+| family | W_sketch | W_raw (same slice) | **reduction (n=3, 95% CI)** | accuracy |
+|---|---|---|---|---|
+| **DDSketch** | **1.00 MB** | 33.7 MB | **33.8× ± 0.3** | p50 0.69% (84%≤ε), p99 2.82% |
+| **HLL** | **0.51 MB** | 33.7 MB | **65.9× ± 0.8** | card 0.33% |
+
+`W_sketch` reproduces the (c) committed numbers (DDSketch 0.99 MB, HLL 0.51 MB) **exactly**,
+which definitively settles the (c′) "57 MB" question: it was the un-sliced 8-metric data, NOT
+a code regression. Reduction CIs are tight (±0.3 / ±0.8 over 3 trials). HLL ships less (one
+register sketch) → ~2× the DDSketch reduction.
+
+#### (c‴) Encoding-factor decomposition vs the gzip baseline (b0-gzip)  ◐
+The honest reviewer question: *does sketching still win after a deployment compresses the raw
+baseline?* Decomposition (DDSketch, real gct):
+
+| | wire | factor |
+|---|---|---|
+| raw OTLP | 33.7 MB | — |
+| **raw + gzip** (b0-gzip) | ~2.7 MB | **encoding 12.3×** (gzip-6 on the actual payload) |
+| **ASAP sketch** | 1.0 MB | total **33.8×** vs raw |
+| **ASAP vs raw+gzip** | — | **net ~2.7×** (= 33.8 / 12.3) |
+
+So ASAP's 33.8× decomposes as **encoding (12.3×, free to anyone with gzip) × residual
+aggregation (~2.7×, ASAP-unique)** — `12.3 × 2.7 ≈ 33.8`, internally consistent. **Sketching
+still beats a gzip-compressed raw baseline by ~2.7×**, the aggregation contribution
+compression can't replicate.
+
+**Measurement caveat (honest):** the encoding factor is `gzip(actual payload content)`
+measured **offline** (the single-node cold-off stack is `--network host`, so the
+agent→data-plane leg can't be isolated from the constant replay→agent leg on `lo` — the
+attempted loopback-byte method failed, all arms ≈ 35 MB). The configs `agent-raw-{gzip,zstd}.yaml`
++ `baselines.py` are ready; the *clean on-wire* compression number belongs on the **cluster**
+(Phase-2 per-node NIC isolates agent→backend bytes). Also unmeasured: sketch-envelope
+gzip (binary, compresses less) — so net ASAP-vs-raw+gzip is a conservative lower bound here.
+
 **Two honest, root-caused gaps (not fabricated):**
 - **CountSketch topk recall 0** — real semantic mismatch (orthogonal to timing): warm
   topk **keys by `item` not `host`** and **ranks by occurrence *frequency*, not
@@ -190,22 +306,54 @@ steady** egress cut (mean ~1.3× after first-window + periodic full re-sync).
 
 ---
 
-## Fig 6 — Edge CPU / memory: sketch vs raw, + long soak  ◻
+## Fig 6 — Edge CPU / memory: sketch vs raw, + long soak  ✅ edge bounded · ⚠ backend leak found
 **Claim (dims 2–3):** sketch edge CPU/RSS bounded vs raw-forwarding; no leak over 24 h.
-**Layout:** (a) stacked CPU bar per baseline (raw `b0` / gzip `b1` / sketch `b3`);
-(b) RSS-over-time line, 24 h, slope-based leak verdict.
+**Layout:** (a) stacked CPU bar per baseline (raw `b0` / sketch `b3`);
+(b) RSS-over-time line, slope-based leak verdict.
 **Note (honest framing):** this is the **sketch-vs-raw** CPU story — *not* a
 sampling-CPU claim. Sampling's win is bandwidth/ingest; the edge-CPU lever is
-sketch-vs-raw **+ the CMS empty-base delta opt (−63%)**. **◻ need the raw baseline + soak.**
+sketch-vs-raw **+ the CMS empty-base delta opt (−63%)**.
+
+**Measured (real gct, cold-OFF, constant 5000 pts/s, 30-min soak = 9.5M pts / 0
+errors; 24 h figures are linear extrapolations of the measured slope):**
+
+**(a) CPU / RSS — both arms measured (raw arm NOT skipped):**
+| arm | mean CPU% | p99 CPU% | steady RSS | n |
+|---|---|---|---|---|
+| b0 raw-forward edge (no asap_edge) | 2.75 | 3.33 | 207 MB | 60 |
+| b3 sketch edge (DDSketch+Sum) | 4.38 | 8.19 | 223 MB | 360 |
+| b0 data_plane | 7.69 | 8.13 | 28 MB | 60 |
+| b3 data_plane | 0.26 | 0.60 | 25→72 MB | 360 |
+
+Sketch edge costs **~1.6× CPU and +16 MB RSS** vs raw-forward — bounded, as claimed.
+
+**(b) Leak slope (edge and data_plane separately):**
+- **Edge: BOUNDED** — **+2.6 MB/h (≈0)**, flat ~223 MB the whole soak (24 h extrap +63 MB). ✅
+- **data_plane: CLIMBING (monotone, linear)** — **+89.9 MB/h** (/proc) / +85.2 MB/h (the
+  binary's own `MEMORY_DIAG` gauge — two independent sources agree); 25→72 MB over
+  30 min, no plateau (24 h extrap ~+2 GB). ⚠
+
+**Stale-sid finding ([[gct-memory-findings]]) — mechanism refined:** SketchStore **sid
+count plateaus hard at 1004** (the cardinality cap) — the "sid count keeps growing"
+reading does **not** reproduce. But **per-sid warm-sketch state grows unbounded**
+(`MEMORY_DIAG` payload 257 KB → 37.6 MB, **~146×**) and drives the backend RSS climb
+~1:1. So the backend memory growth the prior note flagged is **real and reproduces**;
+the driver is **per-sid DDSketch-state growth the evictable flusher doesn't reclaim
+under steady load**, not sid-count growth. Retention is **backend-side** — edge is flat.
+**Single-node loopback. Artifacts:** `datasets_eval/soak/` (`soak_RESULTS.md`,
+`rss_over_time.png`, `summary.json`, raw samples), branch `feat/soak-fig6`.
+**Follow-up:** the backend per-sid state growth is a real defect worth a fix (the
+flusher's evictable accounting under sustained ingest).
 
 ---
 
-## Fig 7 — Query latency CDF: PromQL-native vs sketch-answered  ✅ (warm measured; cold-fallback arm blocked)
-**Claim (dim 5):** warm-tier p50/p99 production-usable; cold fallback ≤2×.
-**Layout:** latency CDF, lines for B0 (native) vs B3 (sketch warm) vs cold-fallback.
-**Measured (real gct, cold-OFF stack, wall-clock-anchored, 599-query mix @15 QPS,
+## Fig 7 — Query latency CDF: PromQL-native vs sketch-answered  ✅ (warm + cold-fallback both measured)
+**Claim (dim 5):** warm-tier p50/p99 production-usable; cold fallback bounded.
+**Layout:** latency CDF, lines for warm sketch tier vs cold-fallback archive.
+
+**Warm arm** (real gct, cold-OFF stack, wall-clock-anchored, 599-query mix @15 QPS,
 guard-verified before timing — DDSketch read returned 691 real warm series,
-`sum`=exact GT 216.3535, all `data_source=asap_query`, 0 empties/errors):**
+`sum`=exact GT 216.3535, all `data_source=asap_query`, 0 empties/errors):
 
 | query kind | p50 | p95 | p99 | n |
 |---|---|---|---|---|
@@ -213,16 +361,43 @@ guard-verified before timing — DDSketch read returned 691 real warm series,
 | `quantile_over_time` (DDSketch, 691-series reconstruction) | 18.34 | 19.56 | 20.65 ms | 400 |
 | `sum` (lossless) | 1.58 | 2.19 | 2.25 ms | 199 |
 
-CDF is **bimodal**: cheap lossless `sum` at ~1.5–2.3 ms, 691-series DDSketch
-quantile reconstruction at ~18–21 ms; tails tight (p99 within ~1 ms of p50 per
-kind). Headline: warm sketch-answered PromQL is production-usable single-digit-to-
-~20 ms server-side. **Cold-fallback arm attempted but blocked** (cold ship rides
-the disabled control channel → MinIO stayed empty, old-ts queries still served
-warm) → **warm-only reported, not faked**. **Single-node loopback — server-side
-latency only, no network RTT.** `count_over_time` excluded (does not resolve on the
-warm path — falls through to empty archive). Artifacts: `datasets_eval/latency/`
-(`latency_RESULTS.md`, `latency_cdf.png`, `per_query_latency.json`,
-`compute_latency.py`), branch `feat/query-latency-cdf`.
+**Cold-fallback arm** (NOW MEASURED — real gct, cold-ON stack: MinIO + gorilla-merger
++ Thanos store-gateway/query + data-plane with `ASAP_THANOS_QUERY_URL`; cold-enabled
+edge with full `cold.ship_endpoint` block; 600-query mix @15 QPS, guard-verified —
+**600/600 `data_source=thanos_query`**, 0 empties/errors, so every timed query was
+answered by the cold/archive engine, not a warm shortcut):
+
+| query kind | p50 | p95 | p99 | n |
+|---|---|---|---|---|
+| all (mix) | 22.28 | 47.17 | 67.07 ms | 600 |
+| `quantile_over_time` (1000-series, Thanos PromQL over raw archived samples) | 24.27 | 48.59 | 68.41 ms | 400 |
+| `sum` (lossless, archive) | 15.48 | 33.33 | 42.74 ms | 200 |
+
+Warm CDF is **bimodal** (cheap lossless `sum` ~1.5–2.3 ms, 691-series DDSketch
+quantile reconstruction ~18–21 ms; tails tight). Cold CDF sits to the right with a
+longer tail: the archive answer crosses data-plane → thanos-query → gorilla-merger
+StoreAPI + store-gateway and re-evaluates PromQL over raw Gorilla-XOR samples.
+**Headline: warm p50/p99 = 18.3/20.0 ms (production-usable); cold-fallback p50/p99 =
+22.3/67.1 ms — overall p50 ≈ 1.2× warm, p99 ≈ 3.3× warm.** So the cold path stays in
+the tens of ms (no order-of-magnitude blowup), at the cost of a heavier p99 tail than
+the warm tier.
+
+How the cold path was forced & verified (the prior blocker is resolved): cold ship is
+decoupled from the control channel (PR #500), so a static edge with `cold.enabled:true`
+ships whenever `cold.ship_endpoint` is set — the multisketch coldon config had only
+`cold:{enabled:true}` with NO ship_endpoint, which the edge treats as **drain-only**
+(`config.go`: "Empty => drain-only (no shipping)") → that was why MinIO stayed empty.
+With a complete cold block the edge shipped 1000-series ASAPFRG1 fragments to the
+merger (verified via per-shard `cold drain`/`shipBatch` logs and thanos `count=1000`).
+A cold storage-routing table pins `google_cluster_2019_cpu_rate → gorilla_object_store`
+so its instant queries dispatch to the ThanosQueryEngine; the control-plane was stopped
+during timing because it periodically re-POSTs a storage-routing table that overrides
+the file table back to warm. **Single-node loopback — server-side latency only, no
+network RTT.** Artifacts: `datasets_eval/latency/` (`latency_RESULTS.md`,
+`latency_cdf.png`, `latency_summary.json`, `per_query_latency.json`,
+`per_query_latency_cold.json`, `compute_latency.py`, `cold_latency_replay.py`,
+`stack-coldon.sh`, `backend-storage-routing-coldon.yaml`, `agent-cold-ship.yaml`,
+`queries-latency-cold.json`), branch `eval/fig7-cold-arm`.
 
 ---
 
@@ -236,12 +411,64 @@ per-layer CPU/mem bars. **◐**
 
 ---
 
-## Fig 9 — Coordinated vs uniform `p` on a skewed fleet  ◐
-**Claim:** `p_i ∝ √(f_i/rate_i)` beats uniform-`p` at equal merged variance, with the
-gap ∝ the rate CV; the win **only appears on skewed fleets** (multi-edge).
-**Layout:** total edge work (or wire bytes) for coordinated vs uniform across rate-CV.
-**Have:** the differentiated grant — hot edge `p=0.0065`, quiet `p=1.0` (ε-floors
-0.0079 vs 0.444); single-edge ⇒ p=1 by design. **Need:** the CV sweep on ≥2 edges. **◐**
+## Fig 9 — Coordinated ε-floor `p` vs fixed-p (NitroSketch)  ✅ (algorithm) / ◐ (system)
+**Claim (corrected):** the unified **whole-sketch** ε-floor `p = 1/(1+ε²·R)` (`R` = total
+update rate, OBSERVABLE) gives a *principled* sampling rate that (i) cuts insert cost ~`1/p`
+while bounding the whole-sketch L2 error to ε, and (ii) in a fleet adapts `p_i` to each edge's
+own `R_i`. It is the SAME sampler as NitroSketch — the contribution is *deriving* `p` from
+`ε`+`R` (not a hand-tuned knob), not a per-key accuracy win. (Per-key `√(f/rate)` retired.)
+
+### (a) Standalone algorithm comparison — REAL DEBS-2022, on a real CMS  ✅ (corrected)
+> **The earlier per-key version of this result was WRONG and is retracted.** It set
+> `p_k=1/(1+ε²·f_k)` using the oracle per-key `f_k` — circular (knowing every `f_k` = exact
+> counting, no sketch needed), and it resurrected the RETIRED per-key `√(f/rate)` allocation.
+> The unified ε-floor is **whole-sketch**: ONE `p=1/(1+ε²·R)`, `R`=total update count (OBSERVABLE).
+
+`epsilon_floor_vs_nitro_test.go` — real CMS over real DEBS (5,493 keys, **R=54M** updates,
+observable). Whole-sketch ε-floor `p=1/(1+ε²R)` vs exact.
+
+**(A) throughput / memory** (CMS 5×4096, sketchlib geometric skip-sampler):
+
+| metric | exact (p=1) | ε-floor (ε=0.05, p=7.4e-6) | note |
+|---|---|---|---|
+| **insert throughput** | 8.3 Mupd/s | **183 Mupd/s (22×)** | the NitroSketch win — skip-sampling cuts update work ~1/p |
+| **memory** | 164 KB | 164 KB (constant in #keys) | vs exact key→count map 179 KB; CMS win is *asymptotic* |
+| **query latency** | 96 ns/key | 76–82 ns/key | unchanged (CMS query is O(rows)) |
+
+**(B) accuracy law — what the ε-floor actually bounds** (CMS 5×65536 to isolate sampling from
+collisions; mean of 5 seeds). The ε-floor bounds the **additive AGGREGATE** (F1=R) to ε; a
+**point query on key `k`** is protected only to `ε·√(R/f_k)`:
+
+| ε | **F1-total rel-err** (the bounded aggregate) | heaviest key (f≈1.5M) | rare keys (f≲1e3) |
+|---|---|---|---|
+| 0.05 | **0.050** (= ε ✓) | 0.26 (pred ε√(R/f)=0.30) | → 1.0 (lost) |
+| 0.10 | **0.080** (≈ ε ✓) | 0.39 (pred 0.61) | → 1.0 (lost) |
+
+So the honest, defensible claim is: **`p` derived from ε+observable R gives 22× insert
+throughput while holding the whole-sketch aggregate error at ≈ε** — same sampler as NitroSketch,
+the win is *how `p` is set* (not a hand-tuned knob). Per-key accuracy follows `ε·√(R/f_k)`:
+**heavy hitters survive, rare keys fall below the sampling floor — inherent to sampling, not a
+defect.** The right accuracy metric is therefore the aggregate / heavy-hitter error, never
+rare-key rel-err.
+
+### (a2) Per-edge fleet — the differentiation, on a skewed fleet  ✅
+Real DEBS has only **3 exchanges** (mild skew → fixed-p worst-edge only **1.3×** the ε-floor's),
+too weak to show it. On a **synthetic fleet** (64 edges, Zipf rates over ~2 decades,
+rate-CV=2.9), at matched total bandwidth:
+
+| | per-edge F1 sampling-err (analytical) | empirical F1 (real CMS, 5 seeds) |
+|---|---|---|
+| **ε-floor** (`p_i=1/(1+ε²R_i)`) | median 0.050, **max 0.050** (uniform) | hot 0.020 / **cold 0.020** |
+| **fixed-p** (matched bw) | median 0.103, **max 0.162** | hot 0.010 / **cold 0.148** |
+
+`→` fixed-p **over-protects the hot edge and under-protects the cold edge by 7×** (cold-edge
+0.148 vs 0.020); the ε-floor spends the same total bandwidth but equalizes error at ε across
+the fleet. This is the per-edge adaptation the coordinator grants live (see (b)).
+
+### (b) System-level differentiated grant  ◐
+Live on the cluster: hot edge `p=0.09`, quiet `p=0.14` (coordinator-granted ε-floor from
+the autonomous `/plan/auto` loop, PR #382). **Need:** the rate-CV sweep on ≥2 edges for the
+system figure.
 
 ---
 
@@ -384,8 +611,9 @@ high-sample-per-window"). This requirement extends the *same* optimizer to alloc
    on it. (The wire/TCO cost model is the decision function.)
 2. **sketch type + `(W, L, agg_type)`** — the existing bind-rules + cost output.
 3. **sampling `p`** — which sketches are sampling-eligible (policy from the
-   controller) + the coordinated runtime allocation `p_i ∝ √(f_i/rate_i)` (data_plane
-   coordinator, ε-floored).
+   controller) + the coordinated runtime allocation, the whole-sketch ε-floor
+   `p_i = 1/(1+ε²·rate_i)` (data_plane coordinator; the per-key `√(f/rate)` law
+   was retired).
 
 ### Methodology — how to evaluate the 4-tuple `{sketch, size, p, ε_cdm}`
 
@@ -487,8 +715,8 @@ coordinated sampling, topk, the ε-gate/delta regime). Driver+data:
 | 6.1 | threshold alert (Fig 5) | ✅ |
 | 6.2 | bandwidth ablation W×L×enc×p (Fig 2) | ◐ (p + encoding done; W, L to run) |
 | 6.headline | Pareto (Fig 1) | ◐ (corners done; one combined sweep) |
-| 6.2 | edge CPU/mem + soak (Fig 6) | ◻ |
-| 6.4 | query latency CDF (Fig 7) | ✅ warm (cold-fallback blocked) |
+| 6.2 | edge CPU/mem + soak (Fig 6) | ✅ edge bounded (+2.6 MB/h); ⚠ backend leak +90 MB/h |
+| 6.4 | query latency CDF (Fig 7) | ✅ warm + cold-fallback (both measured) |
 | 6.3 | cross-layer placement (Fig 8) | ◐ (design+proof; bars to run) |
 | 6.x | coordinated vs uniform (Fig 9) | ◐ (differentiation shown; CV sweep) |
 | 6.x | scaling N∈{1,10,100} (Fig 10) | ◻ |
@@ -501,3 +729,433 @@ defensible numbers (incl. a real-workload trace). The *cost-baseline* half (raw-
 sketch CPU/mem, latency CDF) and the *multi-node* half (scaling, coordinated-vs-
 uniform CV sweep) are the runs that remain — and the multi-node axis is the one the
 coordinated-sampling claim most needs.
+
+
+---
+
+<!-- merged from docs/eval-query-plan.md — the per-dataset query list that feeds the figures -->
+
+## Evaluation query plan — the queries to run on the top-5 datasets
+
+> **Audience:** anyone building or running the §6 evaluation. For *each of the five
+> datasets* shortlisted in
+> [`use-case-dataset-survey.md` §5b](use-case-dataset-survey.md), this doc writes down
+> the **concrete queries we plan to run** — warm (sketch-answered) and cold (exact
+> replay) — tagged to the routing **mode** (M1/M2), the **sketch families**, the **seven
+> axes**, and the **§6 figure/claim** each query feeds
+> ([`evaluation-plan-figures.md`](evaluation-plan-figures.md)).
+>
+> Companion to [`use-case-dataset-survey.md`](use-case-dataset-survey.md) (modes +
+> warm-eligibility predicate, §0a; the top-5 + scale, §5b),
+> [`pipeline-query-catalog.md`](pipeline-query-catalog.md) (what the pipeline can answer
+> today) and [`sketch-algebra-query-mapping.md`](sketch-algebra-query-mapping.md)
+> (query → sketch algebra).
+>
+> **Status:** query-plan reference, 2026-06-14. Datasets 1 (Google cluster) and 2
+> (DEBS-2022) are **wired** (`datasets_eval/google_cluster/`, `datasets_eval/debs/`);
+> 3–5 are the **build-out** — the queries below are the spec their harnesses implement.
+
+---
+
+## 0. Conventions — full evaluation = query × **every** supported sketch
+
+**Full evaluation runs each warm query against *every* sketch that supports its kind**,
+on identical data, and scores each independently — so every query is a **head-to-head
+across sketch families** (the Fig 3c DDSketch-vs-KLL comparison, generalised to all
+kinds and all datasets). A query of kind *K* expands to one run per sketch in the
+"all supported sketches" column below:
+
+| query kind | **all supported sketches (each run + scored)** | metric suffix per sketch | comparison reported |
+|---|---|---|---|
+| **quantile** (p50/p99/…) | **DDSketch**, **KLL** | `_q_ddsketch`, `_q_kll` | median vs tail rel-err, wire bytes (DD ≈ −26% wire, KLL wins median) |
+| **topk / heavy-hitter** | **CountSketch-heap**, **CountMinSketch-heap** | `_topk_cs`, `_topk_cms` | recall@k, by-count vs by-value-sum, wire |
+| **frequency** (point count of an item) | **CountMinSketch**, **CountSketch** | `_freq_cms`, `_freq_cs` | one-sided (CountMinSketch) vs unbiased (CountSketch) error |
+| **cardinality** (distinct) | **HLL** *(only family)* | `_card_hll` | rel-err vs `1.04/√m` |
+| **sum** | **Sum** *(lossless)* | `_sum` (bare metric) | exact (sanity) |
+| **count** | **Count** *(lossless)* | bare metric | exact (sanity) |
+
+Each warm query is the MetricsQL/PromQL + ground-truth record from
+`datasets_eval/multisketch/queries-*.json`:
+
+```json
+{ "id": "...", "kind": "quantile|topk|frequency|count_unique|sum|count",
+  "metricsql": "quantile_over_time(0.99, <metric>_q_<sketch>[300s])",
+  "sketches": ["ddsketch", "kll"],
+  "gt": { "op": "...", "metric": "...", "...": "..." } }
+```
+
+`<sketch>` is expanded over the `sketches` list at harness time → one scored run each.
+The `gt` is computed from the **raw replay** (exact); we score `rel-err = |warm − gt| /
+|gt|` against the joint envelope `ε_sk + ε_s + ε_cdm` (recall for topk). Default window =
+**300 s tumbling**, wall-clock-anchored (the Fig 3c fix).
+
+**In the per-dataset tables below**, the *sketches run* column lists the full set; the
+metricsql shows the `_<sketch>` slot the harness expands. **Cold queries** are exact
+point/range replay from the Gorilla/`intchunk` archive (M2 mandatory raw + rare M1
+forensic). **M2 short-circuit:** a decision query `agg ⋛ τ` is answered from the warm
+interval `[v−ε, v+ε]` — warm-only if `v+ε < τ` or `v−ε > τ`; only `τ ∈ [v−ε, v+ε]` drills
+cold. Headline M2 metric = **warm-only-resolved fraction**, swept over `τ`, `ε`, and
+*sketch family* (different ε per family → different short-circuit rate).
+
+---
+
+## 1. Google cluster 2019 — **M1** (observability/resource)  ✅ wired
+
+Metrics: `gct_cpu_rate`, `gct_memory_usage`; series key `(machine, job, task/instance)`;
+group labels `zone`/`cell`/`service`. Use case: fleet / per-cell resource SLO dashboards.
+
+### Warm queries (each run on **all supported sketches**)
+
+| id | metricsql (`_<sketch>` expanded) | kind | sketches run (each scored) | axes | feeds |
+|---|---|---|---|---|---|
+| `gct-cpu-p99` | `quantile_over_time(0.99, gct_cpu_rate_q_<sketch>[300s])` | quantile | **DDSketch, KLL** | 2,6 | Fig 3a, Fig 3c, Fig 7 |
+| `gct-cpu-p50` | `quantile_over_time(0.50, gct_cpu_rate_q_<sketch>[300s])` | quantile | **DDSketch, KLL** | 2,6 | Fig 3a, Fig 3c |
+| `gct-cpu-p99-by-cell` | `quantile_over_time(0.99, gct_cpu_rate_q_<sketch>[300s])` *(by `cell`)* | quantile | **DDSketch, KLL** | 2,4,6 | Fig 3, repeated-dashboard |
+| `gct-topk-host` | `topk(10, sum by (machine) (gct_cpu_rate_topk_<sketch>))` | topk | **CountSketch-heap, CountMinSketch-heap** | 2,6 | Fig 3c topk recall (CountSketch-heap vs CountMinSketch-heap) |
+| `gct-freq-service` | `count_over_time(gct_cpu_rate_freq_<sketch>{service="svc-000003"}[300s])` | frequency | **CountMinSketch, CountSketch** | 2,6 | one-sided vs unbiased freq |
+| `gct-card-service` | `count(gct_cpu_rate_card_hll)` | cardinality | **HLL** | 2,6 | Fig 3c per-series distinct |
+| `gct-sum-cpu` | `sum(gct_cpu_rate_sum)` | sum | **Sum** (exact) | 2 | Fig 3c exact check |
+| `gct-sum-mem-by-zone` | `sum by (zone) (gct_memory_usage_sum)` | sum | **Sum** | 2,4 | Fig 3c |
+| `gct-cpu-alert` | `quantile_over_time(0.99, gct_cpu_rate_q_<sketch>[300s]) > 0.8` | threshold | **DDSketch, KLL** | 2,3 | Fig 5 alert / repeated |
+
+### Cold queries (rare forensic — disjoint)
+
+| id | query | feeds |
+|---|---|---|
+| `gct-forensic-point` | exact CPU of `instance=X` at `t=2019-05-12T03:14Z` (range replay) | Fig 11 cold path (rare M1) |
+
+---
+
+## 2. DEBS-2022 — **M2** (finance/tick)  ✅ wired
+
+Metric: `debs_last_price` (Gauge `financial.last_trade_price`, value = `last`), attrs
+`symbol`/`exchange`/`sectype`; `debs_volume`. 5-min tumbling, Berlin wall-clock. Use
+case: live VWAP / price-quantile dashboards + threshold alerts on a **skewed** symbol
+fleet; MiFID audit / backtest on the *same* series → cold.
+
+### Warm queries (each run on **all supported sketches**)
+
+| id | metricsql (`_<sketch>` expanded) | kind | sketches run (each scored) | axes | feeds |
+|---|---|---|---|---|---|
+| `debs-price-p50` | `quantile_over_time(0.50, debs_last_price_q_<sketch>{symbol="ASML.NL"}[300s])` | quantile | **DDSketch, KLL** | 2,7 | Fig 3 accuracy (EMA/VWAP proxy) |
+| `debs-price-p99` | `quantile_over_time(0.99, debs_last_price_q_<sketch>{symbol="ASML.NL"}[300s])` | quantile | **DDSketch, KLL** | 2,7 | Fig 3c per-family |
+| `debs-vwap` | `sum(debs_last_price_sum * debs_volume_sum) / sum(debs_volume_sum)` *(per symbol)* | sum | **Sum** (exact) | 2,7 | VWAP exactness |
+| `debs-roll-vol` | `sum_over_time(debs_volume_sum{symbol="ASML.NL"}[300s])` | sum | **Sum** | 2,4,7 | rolling-volume dashboard |
+| `debs-topk-active` | `topk(10, sum by (symbol) (debs_ticks_topk_<sketch>))` | topk | **CountSketch-heap, CountMinSketch-heap** | 2,7 | most-active-symbol board |
+| `debs-coord-sample` | per-symbol ingest under the ε-floor `p_i = 1/(1+ε²·rate_i)` (skew sweep) | sampling | **DDSketch, KLL** (under `p`) | 1,7 | **Fig 9** coordinated 32× |
+
+### M2 short-circuit (decision queries — warm-first, cold on ambiguity; per sketch)
+
+| id | decision query | sketches | resolves warm-only when | drills cold when |
+|---|---|---|---|---|
+| `debs-move-alert` | `quantile_over_time(0.99, debs_last_price_q_<sketch>{symbol=…}[300s]) > τ` | **DDSketch, KLL** | `v+ε < τ` or `v−ε > τ` | `τ ∈ [v−ε, v+ε]` |
+
+### Cold queries (mandatory raw — cheap via edge Gorilla)
+
+| id | query | feeds |
+|---|---|---|
+| `debs-audit-replay` | trade-by-trade exact replay for `(symbol, window)` (MiFID) | Fig 11 cold (finance-tick edge-Gorilla) |
+| `debs-backtest` | exact tick series for `symbol` over the week | Mode-2 cold half |
+
+---
+
+## 3. Alibaba microservices 2021/2022 — **M1** (observability/traces)  ◻ to add
+
+Metrics: `alibaba_ms_latency` (span duration, per `service`), `alibaba_ms_calls`,
+distinct-caller stream `alibaba_ms_callers`. Use case: per-service p50/p99 latency SLO
+alerting (RED) + distinct-caller cardinality; incident trace replay → cold. **Metric-
+identity split:** the latency *metric* is warm; the raw span archive is a *separate*
+artifact → cold (condition 2 provable).
+
+### Warm queries (each run on **all supported sketches**)
+
+| id | metricsql (`_<sketch>` expanded) | kind | sketches run (each scored) | axes | feeds |
+|---|---|---|---|---|---|
+| `ms-lat-p99` | `quantile_over_time(0.99, alibaba_ms_latency_q_<sketch>{service="S"}[300s])` | quantile | **DDSketch, KLL** | 2,6,7 | **Fig 7** `quantile_over_time` at scale |
+| `ms-lat-p50` | `quantile_over_time(0.50, alibaba_ms_latency_q_<sketch>{service="S"}[300s])` | quantile | **DDSketch, KLL** | 2,6,7 | Fig 3c per-family |
+| `ms-callers-card` | `count(alibaba_ms_callers_card_hll{service="S"})` | cardinality | **HLL** | 2,6 | distinct-caller fan-in |
+| `ms-req-rate` | `sum by (service) (rate(alibaba_ms_calls[300s]))` | count | **Count** (exact) | 2,4,6 | RED rate dashboard |
+| `ms-topk-slow` | `topk(10, sum by (service) (alibaba_ms_latency_topk_<sketch>))` | topk | **CountSketch-heap, CountMinSketch-heap** | 2,6 | slowest-service board |
+| `ms-lat-slo-alert` | `quantile_over_time(0.99, alibaba_ms_latency_q_<sketch>{service="S"}[300s]) > 0.5` | threshold | **DDSketch, KLL** | 2,3 | Fig 5 / repeated SLO |
+
+### Cold queries (separate span artifact — disjoint)
+
+| id | query | feeds |
+|---|---|---|
+| `ms-trace-replay` | exact span tree for `trace_id=…` (incident forensics) | Fig 11 cold (the half anchors lack) |
+
+---
+
+## 4. Azure VM 2019 — **M1** (observability/resource)  ◻ to add
+
+Metrics: `azure_vm_cpu` (per-VM CPU, 5-min); cold counter `azure_vm_billing`
+(**separate** series). ~2.6 M VMs = ~2.6 M series. Use case: per-VM CPU capacity/SLO
+dashboards at fleet scale; billing/chargeback → cold. Textbook metric-identity split;
+stresses the controller-allocation figure.
+
+### Warm queries (each run on **all supported sketches**)
+
+| id | metricsql (`_<sketch>` expanded) | kind | sketches run (each scored) | axes | feeds |
+|---|---|---|---|---|---|
+| `vm-cpu-p99` | `quantile_over_time(0.99, azure_vm_cpu_q_<sketch>[300s])` | quantile | **DDSketch, KLL** | 2,6 | **Fig 12** alloc @ ~2.6 M cardinality |
+| `vm-cpu-p50` | `quantile_over_time(0.50, azure_vm_cpu_q_<sketch>[300s])` | quantile | **DDSketch, KLL** | 2,6 | Fig 3c per-family |
+| `vm-cpu-p99-by-sub` | `quantile_over_time(0.99, azure_vm_cpu_q_<sketch>[300s])` *(by `subscription`)* | quantile | **DDSketch, KLL** | 2,4,6 | Fig 12 / repeated |
+| `vm-card-vms` | `count(azure_vm_cpu_card_hll)` | cardinality | **HLL** | 2,6 | fleet-size distinct |
+| `vm-avg-util-by-sub` | `sum by (subscription) (azure_vm_cpu_sum) / count by (subscription) (azure_vm_cpu)` | sum/count | **Sum, Count** | 2,4,6 | capacity dashboard |
+| `vm-cpu-alert` | `quantile_over_time(0.99, azure_vm_cpu_q_<sketch>[300s]) > 0.9` | threshold | **DDSketch, KLL** | 2,3 | Fig 5 / repeated |
+
+### Cold queries (separate billing series — disjoint)
+
+| id | query | feeds |
+|---|---|---|
+| `vm-billing-replay` | exact per-VM billing counter for `(vm, billing_period)` (dispute-grade) | Fig 11 cold (separate series) |
+
+---
+
+## 5. Binance/Kraken crypto tick — **M2** (finance/tick)  ◻ to add
+
+Metrics: `crypto_trade_price`, `crypto_trade_vol` (per `pair`). Hundreds of pairs, 24/7,
+very-high per-series frequency. Use case: live crypto VWAP/quantile alerts + "is this
+pair behaving oddly?" triage; strategy **backtest** replays raw on the *same* series →
+cold. The Mode-2 / short-circuit showcase + the two orthogonal edge compressions.
+
+### Warm queries (each run on **all supported sketches**)
+
+| id | metricsql (`_<sketch>` expanded) | kind | sketches run (each scored) | axes | feeds |
+|---|---|---|---|---|---|
+| `cx-price-p50` | `quantile_over_time(0.50, crypto_trade_price_q_<sketch>{pair="BTCUSDT"}[300s])` | quantile | **DDSketch, KLL** | 2,7 | Fig 3 accuracy (high-freq) |
+| `cx-price-p99` | `quantile_over_time(0.99, crypto_trade_price_q_<sketch>{pair="BTCUSDT"}[300s])` | quantile | **DDSketch, KLL** | 2,7 | Fig 3c per-family |
+| `cx-vwap` | `sum(crypto_trade_price_sum * crypto_trade_vol_sum) / sum(crypto_trade_vol_sum)` *(per pair)* | sum | **Sum** (exact) | 2,7 | VWAP exactness |
+| `cx-roll-vol` | `sum_over_time(crypto_trade_vol_sum{pair="BTCUSDT"}[300s])` | sum | **Sum** | 2,4,7 | rolling-volume board |
+| `cx-topk-active` | `topk(10, sum by (pair) (crypto_trades_topk_<sketch>))` | topk | **CountSketch-heap, CountMinSketch-heap** | 2,7 | most-active-pair board |
+
+### M2 short-circuit (the headline metric for this dataset; per sketch)
+
+| id | decision query | sketches | resolves warm-only when | drills cold when |
+|---|---|---|---|---|
+| `cx-vol-alert` | `quantile_over_time(0.99, crypto_trade_price_q_<sketch>{pair=…}[300s]) > τ` | **DDSketch, KLL** | `v+ε < τ` or `v−ε > τ` | `τ ∈ [v−ε, v+ε]` |
+| `cx-anomaly-triage` | "which pair/window is anomalous?" → warm screen, then drill | **DDSketch, KLL** | warm screen settles it | flagged window → cold |
+
+Sweep `τ`, `ε`, **and sketch family** → report the **warm-only-resolved fraction** vs
+forced-to-cold (the bound-based short-circuit headline) and the **cold-IO pruning ratio**
+— DDSketch's relative-error bound vs KLL's rank-error bound give *different* ambiguous
+bands, so the short-circuit rate is itself a per-family result.
+
+### Cold queries (mandatory raw — cheap via edge Gorilla)
+
+| id | query | feeds |
+|---|---|---|
+| `cx-backtest` | exact tick series for `pair` over N months (strategy replay) | Mode-2 cold half; edge-Gorilla cheap-cold |
+
+---
+
+## 6. What each dataset's query set proves (coverage)
+
+| dataset | mode | warm query kinds × sketches | cold query | headline figure/claim |
+|---|---|---|---|---|
+| Google cluster 2019 | M1 | quantile {DD,KLL}, topk {CS-heap, CMS-heap}, freq {CMS, CS}, card {HLL}, sum, threshold | rare forensic point | Fig 3 accuracy, Fig 7 latency, Pareto |
+| DEBS-2022 | M2 | quantile {DD,KLL}, VWAP {Sum}, topk {CS-heap, CMS-heap}, **coordinated sampling** | audit/backtest replay | **Fig 9** 32× sampling, edge-Gorilla cold |
+| Alibaba microservices | M1 | quantile {DD,KLL} @scale, card {HLL}, rate {Count}, topk {CS-heap, CMS-heap} | trace replay (separate span) | high-card disjoint (Fig 11), Fig 7 |
+| Azure VM 2019 | M1 | quantile {DD,KLL} @~2.6 M, card {HLL}, sum/count, threshold | billing replay (separate series) | **Fig 12** controller alloc |
+| Crypto tick | M2 | quantile {DD,KLL}, VWAP {Sum}, topk {CS-heap, CMS-heap}, **short-circuit** | backtest replay | **bound-based short-circuit** per family |
+
+**Read:** every dataset runs the **same query kinds against the same full sketch sets**
+(quantile → DDSketch + KLL, topk → CountSketch-heap + CountMinSketch-heap, frequency → CountMinSketch +
+CountSketch, cardinality → HLL, sum/count → lossless) — so each query is a per-dataset
+**head-to-head across families** on identical real data. What differs is the **mode**
+(M1 disjoint vs M2 co-resident) and therefore whether the cold query is a *rare disjoint
+forensic* (M1) or a *first-class, frequently-paired exact replay* (M2); the two M2 sets
+additionally run the **short-circuit decision protocol**, whose warm-only-resolved
+fraction is itself reported **per sketch family** (different ε ⇒ different ambiguous band).
+
+---
+
+## 7. References
+
+- [`use-case-dataset-survey.md`](use-case-dataset-survey.md) — modes + predicate (§0a), top-5 + scale (§5b).
+- [`evaluation-plan-figures.md`](evaluation-plan-figures.md) — Fig 3/5/7/9/11/12, the joint bound.
+- [`pipeline-query-catalog.md`](pipeline-query-catalog.md) — what the pipeline answers today.
+- [`sketch-algebra-query-mapping.md`](sketch-algebra-query-mapping.md) — query → sketch algebra.
+- `datasets_eval/multisketch/queries-*.json` — the warm-query record format reused here.
+- `datasets_eval/debs/DEBS_2022/02_benchmark_queries.md` — the DEBS Q1–Q13 benchmark queries.
+
+---
+
+*End of query plan.*
+
+---
+
+<!-- merged from docs/eval-instrumentation-notes.md — what each sweep-CSV column means -->
+
+## Eval instrumentation notes — what each sweep CSV column means
+
+_Last updated: 2026-05-05 (paper blocker #3 closeout)._
+
+This is the column-by-column "what is this number, where does it
+come from, and why" reference for `deploy/eval-results/sweep-*.csv`
+(the file format produced by
+`deploy/mvp-singlenode/scripts/measure-baseline.py` and chained together by
+`deploy/mvp-singlenode/scripts/run-baseline-sweep.sh` /
+`deploy/mvp-singlenode/scripts/run_e2e_sweep.sh`).
+
+The next person to add a column or interpret one in a paper figure
+should be able to land on this doc and understand the source of
+truth without re-deriving it from comments scattered through
+`measure-baseline.py`.
+
+## Column map
+
+Columns are emitted in the order shown in the CSV header. The
+"source" column says which signal feeds it; the "fallback" column
+is what `measure-baseline.py` consults when the primary path is
+NaN. "Stack tier" is which container the signal originates from.
+
+| Column | Source (primary) | Fallback (paper blocker #3) | Stack tier | Unit / scale |
+| --- | --- | --- | --- | --- |
+| `baseline` | `--baseline` CLI arg (e.g. `b3-delta`) | — | — | label |
+| `scale` | `--scale` CLI arg (e.g. `N1`, `N10`) | — | — | label |
+| `rate` | `--rate` CLI arg | — | — | events/s (workload knob) |
+| `cardinality` | `--cardinality` CLI arg | — | — | distinct series (workload knob) |
+| `producer_cpu_cores` | `docker stats` CPU% / 100 on `--producer-container` | — | producer (`otel-app`) | cores |
+| `producer_rss_mib` | `docker stats` mem on `--producer-container` | — | producer | MiB |
+| `producer_bytes_out_per_s` | `docker stats` net tx delta on `--producer-container`, divided by `--bytes-sample-window` | — | producer | bytes/s on the wire |
+| `agent_cpu_cores` | `rate(otelcol_process_cpu_seconds_total{job="agents"})` | — | agent | cores |
+| `agent_rss_mib` | `otelcol_process_memory_rss_bytes{job="agents"}` / 1MiB | — | agent | MiB |
+| `agent_in_kib_per_s` | `rate(otelcol_asapcollector_processor_input_bytes_total{job="agents"})` / 1024 | `docker stats` net rx avg across `docker-compose-agent-*` / 1024 | agent | KiB/s |
+| `agent_out_kib_per_s` | `rate(otelcol_asapcollector_processor_output_bytes_total{job="agents"})` / 1024 | `docker stats` net tx avg across `docker-compose-agent-*` / 1024 | agent | KiB/s |
+| `agent_points_per_s` | `rate(otelcol_receiver_accepted_metric_points_total{job="agents"})` | — | agent | points/s |
+| `gateway_cpu_cores` | `rate(otelcol_process_cpu_seconds_total{job="gateway"})` (with v0.108 fallback) | — | gateway | cores |
+| `gateway_rss_mib` | `otelcol_process_memory_rss_bytes{job="gateway"}` / 1MiB | — | gateway | MiB |
+| `gateway_points_per_s` | `rate(otelcol_receiver_accepted_metric_points_total{job="gateway"})` (with v0.108 fallback) | — | gateway | points/s |
+| `gateway_out_series_per_s` | `rate(otelcol_exporter_sent_metric_points_total{job="gateway"})` (with v0.108 fallback) | — | gateway | series/s |
+| `backend_cpu_pct` | `docker stats` CPU% on `docker-compose-backend-1` | — | backend | percent of one core |
+| `backend_rss_mib` | `docker stats` mem on `docker-compose-backend-1` | — | backend | MiB |
+| `backend_samples_per_s` | `rate(asap_ingest_samples_total)` (backend `:9091/metrics`) | `rate(otelcol_exporter_sent_metric_points_total{exporter=~"otlp.*backend.*",job="gateway"})` | backend (or gateway when fallback) | samples/s |
+| `backend_query_p99_ms` | `1000 * histogram_quantile(0.99, rate(asap_query_duration_seconds_bucket))` (backend `:9091/metrics`) | `--replay-jsonl PATH` → p99 of successful `duration_ms` rows in client JSONL | backend (server-side) or replay client (client-side) | ms |
+
+## Caveats — do not paper over these
+
+### `agent_in_kib_per_s` / `agent_out_kib_per_s`: in-process bytes vs wire bytes
+
+The patched-processor counter
+(`otelcol_asapcollector_processor_*_bytes_total`) measures the
+**OTLP protobuf MessageSize of the in-process `pmetric.Metrics`
+batch as it crosses the processor boundary**, computed by the
+`pmetric.ProtoMarshaler` in
+`opentelemetry-collector-patch/processor/selfmonitor/selfmonitor.go`.
+That's not the same number as bytes-on-the-wire:
+
+  * It excludes gRPC framing, HTTP/2 headers, and the OTLP
+    request envelope.
+  * For sketch payloads (DDSketch / HLL / etc.), the in-process
+    bytes include the typed proto envelope's full state — the
+    same payload that goes on the wire — so the two measures
+    agree to within ~5%.
+  * For raw / Gorilla / Serf baselines that have no patched
+    processor, no in-process counter fires at all. The fallback
+    is `docker stats` net rx/tx on the agent container, which
+    IS the on-the-wire rate.
+
+When comparing across baselines (the bandwidth claim in
+`docs/paper-outline.md` claim #1), prefer the `docker stats`
+fallback as the apples-to-apples ground truth — note this
+explicitly in the figure caption. The patched-processor counter
+is the right signal for "bytes the sketch processor saw"; the
+wire bytes are the right signal for "bytes the bandwidth budget
+spent."
+
+### `backend_samples_per_s`: backend ingest vs gateway egress
+
+Today's `asap/query-backend:dev` image does NOT expose
+`asap_ingest_samples_total` to Prometheus. The `:9091/metrics`
+surface only contains query-side counters
+(`asap_query_duration_seconds`, `asap_query_requests_total`).
+The fallback uses
+`otelcol_exporter_sent_metric_points_total{exporter=~"otlp.*backend.*",job="gateway"}`,
+which counts the metric points the gateway forwarded to the
+backend over OTLP. Modulo dropped batches (a small number
+tracked elsewhere), gateway-egress equals backend-ingress, so
+this is the right proxy.
+
+For raw / Gorilla / Serf baselines that drop the OTLP forward
+(`drop_original: true` in the agent yaml), the gateway is
+literally not receiving anything from the agent — so this
+fallback returns 0, which is correct: those baselines write
+their compressed output to a local container directory, not to
+the backend. The bandwidth signal for those baselines lives in
+`agent_*_kib_per_s` (docker-stats fallback).
+
+The proper fix is a backend-side counter — that's tracked as a
+follow-up in the ASAPQuery-backend repo. Fallback is good
+enough for the paper.
+
+### `backend_query_p99_ms`: server-side vs client-side p99
+
+`measure-baseline.py` chooses the client-side fallback whenever
+`--replay-jsonl PATH` is provided, because:
+
+  * The Prometheus path dies with the stack
+    (`docker compose down -v` between cells in
+    `run_e2e_sweep.sh`), so the histogram is gone before the
+    next cell can inspect it. The replay JSONL persists on the
+    host filesystem.
+  * Client-side latency is what the caller actually
+    experienced, including any network / OTLP serialization
+    delay — which IS what the paper claim ("query latency
+    competitive with raw") is about.
+
+The numbers will not be identical: client-side adds the
+local-loopback HTTP round-trip (~0.1-0.5ms on the dev box).
+For the paper figure, document which side the number came from
+in the caption. `run_e2e_sweep.sh` always passes
+`--replay-jsonl`, so e2e-sweep CSVs are always client-side p99.
+`run-baseline-sweep.sh` is client-side only when
+`DRIVE_QUERIES=1` is set.
+
+### `asap_query_duration_seconds` only fires on serviced queries
+
+Empty-but-running stack → the histogram has 0 buckets. So the
+primary-path PromQL returns NaN for any cell where no queries
+were issued during the soak. That's not a bug, that's the
+metric's contract; just make sure the sweep driver issues some
+queries before reading the column. `run_e2e_sweep.sh` does this
+by default (it runs `metricsql_replay.py` for the full soak).
+`run-baseline-sweep.sh` did NOT prior to 2026-05-05; the
+`DRIVE_QUERIES=1` opt-in flag added in this paper-blocker-#3
+work fills the gap.
+
+## Signal coverage by baseline (post paper-blocker-#3)
+
+| Baseline | `agent_*_kib_per_s` | `gateway_*` | `backend_samples_per_s` | `backend_query_p99_ms` |
+| --- | --- | --- | --- | --- |
+| `b0a-raw-stream` | docker stats fallback | direct | gateway-fallback | client-side (when DRIVE_QUERIES) |
+| `b0b-raw-batched` | docker stats fallback | direct | gateway-fallback | client-side (when DRIVE_QUERIES) |
+| `b1-serf` | docker stats fallback | direct (zero — drop_original) | gateway-fallback (zero) | client-side (no warm answers — cold path) |
+| `b2-full` | direct (sketch processor) | direct | direct (when ingest counter exists) or gateway-fallback | direct (when histogram is fed) |
+| `b3-delta` | direct (sketch processor) | direct | direct or gateway-fallback | direct or client-side |
+| `b4-tunable` | direct (sketch processor) | direct | direct or gateway-fallback | direct or client-side |
+| `b5-gorilla` | docker stats fallback | direct (zero — drop_original) | gateway-fallback (zero) | client-side (no warm answers — cold path) |
+
+Note that B1/B5 baselines are **expected** to show ~0
+gateway-egress and 0 backend-samples: their pipeline is
+`drop_original: true`, so the wire path to backend is empty by
+design — the bandwidth signal lands in `agent_*_kib_per_s`
+(docker-stats fallback) or in compressed-blob-on-disk metrics
+that aren't in the CSV today.
+
+## Adding a new column
+
+1. Define the source. Prefer Prometheus over `docker stats`
+   (Prom is rate-aware; docker stats requires the two-sample
+   pass).
+2. If the source isn't universal across baselines, add a
+   fallback to `FALLBACK_QUERIES` in
+   `deploy/mvp-singlenode/scripts/measure-baseline.py` (or in `main()` for
+   non-Prom fallbacks). Don't silently let the column NaN —
+   one of the bandwidth-claim figures was unreproducible for a
+   week because of exactly that.
+3. Add a row to the column map above. Mention any unit subtlety
+   in the caveats section.
+4. Smoke-test by running
+   `deploy/mvp-singlenode/scripts/run-baseline-sweep.sh DRIVE_QUERIES=1` and
+   confirming the column has no NaN for any baseline.
diff --git a/docs/gos-eval-results.md b/docs/gos-eval-results.md
new file mode 100644
index 000000000..927f51275
--- /dev/null
+++ b/docs/gos-eval-results.md
@@ -0,0 +1,286 @@
+# GOS Evaluation Results
+
+Measured results for the GOS framework (design-gos-unified-edge-telemetry.md).
+Three axes: (1) anisotropic-vs-isotropic delta communication `ρ`; (2) geometric
+vs distributed F2 monitoring; (3) the Woodruff–Zhang `k/ε²` reference.
+
+Reproduce: `deploy/mvp-multinode/scripts/f2_monitor_eval.sh` (F2), and
+`go test ./sketches/ -run TestGosAnisoSavingsRatio -v` (ρ).
+
+---
+
+## 1. Anisotropic vs isotropic per-cell thresholds (`ρ`)
+
+At equal accuracy budget `B`, the per-cell water-filling gives communication
+`Σ V_j/T_j` never worse than a uniform threshold (Cauchy–Schwarz), by the factor
+`ρ = (Σ√(|g_j|·V_j))² / [(ΣV_j)(Σ|g_j|)] ≤ 1`. Sweeping the cell-magnitude skew
+(Zipf(s) → `g_j=2|Ĉ_j|`, uniform activity), `n = d·w = 1280`, `k = 4`:
+
+| skew `s` | measured `ρ` | predicted `ρ` | comm saving |
+|---|---|---|---|
+| 0.0 (uniform) | 1.0000 | 1.0000 | 0% |
+| 0.5 | 0.9026 | 0.9026 | 9.7% |
+| 1.0 (Zipf) | 0.4966 | 0.4966 | **50.3%** |
+| 1.5 | 0.1283 | 0.1283 | 87.2% |
+| 2.0 (heavy) | 0.0284 | 0.0284 | 97.2% |
+
+The `ρ` above is a **closed-form check**: both columns evaluate the same
+Cauchy–Schwarz factor on a synthetic cell vector, so the exact match confirms
+only that `AllocateThresholds` realizes the water-filling optimum — it is **not**
+a measurement of communication on a real stream (a caveat first raised in the
+code review). The cost of anisotropic mode is the `O(d·w)` edge-memory threshold
+vector; the `GosAnisotropic` toggle exposes the tradeoff.
+
+### Cluster measurement (real agents, iptables byte count)
+
+Replaces the closed-form check with an end-to-end measurement:
+`deploy/mvp-multinode/scripts/gos_aniso_cluster.sh` runs a real asap-otel agent
+(CountSketch `top_endpoint_qps`, `gos_delta_epsilon=0.1`, iso vs `gos_anisotropic:
+true`) fed by the otel-app five-sketch producer with Zipf(`s`) endpoint labels,
+and counts the delta bytes that reach the sink node's `:4317` with an iptables
+counter (kernel-side, exact), over 70 s of steady state per arm after a warmup:
+
+| Zipf `s` | iso bytes | aniso bytes | measured `ρ` |
+|---|---|---|---|
+| 1.1 | 503,230 | 303,901 | **0.60** (40% saving) |
+| 1.5 | 301,851 | 302,328 | 1.00 |
+| 2.0 | 303,693 | 300,198 | 0.99 |
+
+**This is the honest result, and it does NOT match the closed-form story.**
+Anisotropic saves ~40% at *low* input skew (`s=1.1`) but is a wash at higher
+skew — the OPPOSITE of the closed-form prediction that savings grow with skew.
+Two likely confounds, still to be run down: (a) a ~303 KB floor across most
+cells suggests a fixed per-window cost (full keyframe / re-fill after
+window-reset) dominating the *delta* the gate controls; (b) the CountSketch hash
+SPREADS a heavy Zipf endpoint across `d` random-sign cells, so high *input* skew
+does not straightforwardly produce high *cell-magnitude* skew — the quantity the
+water-filling actually exploits. Treat the anisotropic per-cell win as
+**unconfirmed on real workloads** pending this investigation; the mechanism
+(config parse → `applyGosMode` → per-cell `{T_j}`) is verified live end-to-end,
+the payoff is not.
+
+---
+
+## 2. Geometric vs distributed F2 monitoring
+
+Cross-language (Rust coordinator + Go multi-edge driver), `k=4` edges, 20
+sub-window steps, `d=5`, `w=256`, `τ=10⁶`, `ε=0.1`. Total bytes = edge→coord
+sketch bytes + coord→edge `C_ref` broadcast bytes.
+
+**Cluster (real NIC):** the same matrix reproduced across the 8-node CloudLab
+fabric — edges on a source node, coordinator on the WARM node, every ship and
+broadcast crossing the 10 GbE LAN — with byte-identical totals (the protocol is
+deterministic) and the same alert decisions. Reproduce:
+`deploy/mvp-multinode/scripts/f2_wholesketch_cluster.sh` → recorded at
+`deploy/mvp-multinode/eval-8node/f2_wholesketch_cluster.csv`.
+
+| Workload | Mode | Alert | Total bytes | vs distributed |
+|---|---|---|---|---|
+| **stable** (F₂ < τ) | raw (no aggregation) | none ✓ | 3,956 | — |
+| **stable** | distributed | none ✓ | 923,280 | 1.0× |
+| **stable** | **geometric** | none ✓ | **230,820** | **0.25× (4.0× less)** |
+| **ramp** (F₂ crosses τ) | raw (no aggregation) | fired (exact) ✓ | 7,120 | — |
+| **ramp** | distributed | fired @921,600 ✓ | 923,280 | 1.0× |
+| **ramp** | **geometric** | fired @921,600 ✓ | **531,132** | **0.58× (1.74× less)** |
+
+Both sketch modes fire the **same** alert (observed 921,600, inside the
+`[(1−ε)τ, τ) = [900k, 10⁶)` band). Geometric wins in **both** regimes vs
+distributed.
+
+**Raw baseline honesty — and why this workload is protocol-stress, not
+bandwidth-scale.** The raw rows ship every sample as a real msgpack
+`[ts, key, value]` frame (exact-F2 alert ground truth — `f2driver … raw`). On
+the DEFAULT workload raw is the cheapest of all — **by construction, and it
+does not generalize**: the workload has only `H=4` distinct keys, so raw is
+~320 samples (~7 KB), while a sketch ship is a **fixed** `d·w ≈ 11.5 KB`
+regardless of `H`. A sketch only pays off once cardinality is large enough
+that raw exceeds that fixed cost. Measured crossover (`F2_KEYS` sweep, ramp,
+`k=4` edges, 20 steps; distributed is fixed at 923,280 and geometric at
+531,132 — sketch size is `H`-independent):
+
+| `H` distinct keys | raw bytes | raw / distributed | raw / geometric |
+|---|---|---|---|
+| 4 (default) | 7,120 | 0.01× | 0.01× |
+| 256 | 482,960 | 0.52× | 0.91× |
+| **512** | 974,480 | **1.06×** (raw now loses) | 1.83× |
+| 2,048 | 4,007,440 | 4.34× | 7.55× |
+| 8,192 | 16,295,440 | 17.65× | 30.68× |
+| 32,768 | 67,268,880 | 72.86× | 126.65× |
+
+So **raw wins only below ~500 keys** (vs distributed) / ~280 (vs geometric);
+real telemetry cardinality (10³–10⁶ series) puts you deep in the sketch-wins
+regime — consistent with the C1-wire dataset result (33.8×/65.9× reduction on
+the Google-cluster trace). **Two orthogonal claims live here:** (1)
+sketch-vs-raw is a *cardinality* question (settled by the crossover above, and
+by C1-wire at real scale); (2) geometric-vs-distributed is a *monitoring*
+question — ship-on-violation vs ship-every-window. The geometric **ship count**
+(the monitoring decision) is ~`H`-independent (F2 trajectory vs safe zone), but
+its **byte count is not**, because the `C_ref` broadcast size grows with sketch
+density — see the H=2048 run below.
+
+### Realistic cardinality (H=2048, τ auto-scaled) — sketch beats raw, but geometric's broadcast blows up
+
+`f2_wholesketch_cluster.sh` now defaults to `H=2048` distinct keys with
+`τ = 250000·H` (keeps the ramp crossing at the same fractional step; recorded at
+`eval-8node/f2_wholesketch_cluster_h2048.csv`):
+
+| workload | mode | alert | total bytes | vs distributed |
+|---|---|---|---|---|
+| stable | raw | none ✓ | 204,056 | — |
+| stable | distributed | none ✓ | 923,280 | 1.0× |
+| stable | **geometric** | none ✓ | **230,820** | **0.25× (4.0× less)** |
+| ramp | raw | fired ✓ | 4,007,440 | — |
+| ramp | **distributed** | fired ✓ | **923,280** | **1.0× — beats raw 4.3×** |
+| ramp | geometric | fired ✓ | 1,647,966 | **1.78× (LOSES to distributed)** |
+
+Two findings, both honest:
+- **Sketch beats raw at scale.** Ramp distributed (923,280, fixed `d·w`) is
+  **4.3× smaller** than raw (4,007,440) — the cardinality concern is resolved
+  once `H` is realistic.
+- **Geometric's ramp win does NOT survive a dense sketch.** Geometric ramp rose
+  from 531,132 (H=4) to 1,647,966 (H=2048) and now *loses* to distributed. The
+  egress decomposes as `1,647,966 − 28·11,541 (ingress) = 1,324,818 ≈
+  4·28·11,541` — i.e. **the sparse `C_ref` deltas have degenerated into
+  near-full matrices**: 2048 keys in a 1280-cell (`d·w=5·256`) sketch saturate
+  almost every cell, so "changed cells" ≈ "all cells" and the O(k) broadcast
+  amplification returns. Stable geometric still wins 4× (only 4 ships → 4
+  broadcasts), but ramp (28 ships × near-full broadcast) does not. This is
+  exactly the design §12 #1 (anisotropic/thresholded broadcast) + small-norm
+  limit, now *measured*: the geometric protocol pays off when the sketch is
+  **sparse relative to its cell budget**; an overloaded sketch needs a larger
+  `w`, a thresholded (not `Δ≠0`) broadcast gate, or both. The `H=4` run isolates
+  the monitoring logic; this run shows the real-scale communication reality.
+
+**Density sweep — confirms fill ratio, not cardinality, is the cause.** Fixing
+`H=2048` and sweeping the sketch width `w` (ramp, geometric ÷ distributed bytes):
+
+| `w` | fill `H/(d·w)` | distributed | geometric | geo/dist |
+|---|---|---|---|---|
+| 256 | 1.60 (overloaded) | 923,280 | 1,647,966 | 1.78× |
+| 1024 | 0.40 | 3,688,080 | 6,044,478 | 1.64× |
+| 4096 | 0.10 | 14,747,280 | 16,160,883 | 1.10× |
+
+The `geo/dist` ratio falls monotonically as the sketch gets sparser (1.78 → 1.10),
+so **density is the root cause**. But a wider sketch is not the whole fix:
+`distributed` absolute cost explodes with `w` (it ships the full matrix every
+window).
+
+### Thresholded C_ref broadcast gate (design §12 #1) — implemented, and an honest limit
+
+Implemented the OctoSketch-style gate in the coordinator: broadcast only cells
+that moved by more than the §7 F2 per-cell threshold `T = ε‖C‖/(2k√(dw))`, and
+fold **only the shipped cells** back into `last_broadcast` so sub-`T` changes
+accumulate and eventually ship (C_ref error bounded by `T` per cell;
+`f2.rs::sparse_delta_cells_thresholded` + `apply_cells`, tested). **Safe and
+correct:** the `H=4` numbers are byte-identical (531,132 — the gate never fires
+on a sparse sketch), alerts still fire, and the coordinator↔edge references stay
+consistent.
+
+**But its empirical payoff on a Count-Sketch is small** — an honest negative
+result. At `H=2048, w=256` the gate cut geometric ramp only 1,647,966 →
+1,316,158 (~20%), and on a *skewed* Zipf(1.2) ramp it did no better (1.80 M).
+The reason is structural: a Count-Sketch hashes each key into `d` cells with
+**random signs**, and at overload many keys collide per cell, so **input skew
+does not become cell-magnitude skew** — the cell values are homogenized, and a
+threshold has little sparse structure to exploit (unlike Count-Min, where a heavy
+key *is* a heavy cell). So the gate is the right mechanism but the Count-Sketch
+cell homogenization caps its benefit; the effective lever for high cardinality
+remains **sizing `w` to the key count** (keep fill `H/(d·w) ≪ 1`), with the
+threshold gate as a safe, free add-on that helps whenever real cell skew exists.
+
+### Real dataset — DEBS 2022 trading day (no synthetic workload)
+
+Replays the **real DEBS 2022 Grand-Challenge trading-day** feed (Infront
+Financial; 54M market events). Each ticker symbol is a monitored key; the global
+`F2 = Σ_symbol (cumulative event count)²` (trade-concentration / self-join size)
+grows over the day, and the monitor fires when it crosses `τ` ("trading got too
+concentrated"). The first 4M events → 20 sub-windows × 4 edges (symbol→edge by
+hash), `H = 5493` real distinct symbols, `d=5, w=4096` (fill `0.27`, not
+overloaded), `τ = 2.5·10¹⁰` (crosses at step 14). Reproduce:
+`deploy/mvp-multinode/scripts/f2_debs_eval.sh` (preprocessor
+`datasets_eval/debs/scripts/debs_f2_trace.py` + f2driver `F2_TRACE` replay),
+recorded at `eval-8node/f2_debs.csv`:
+
+| mode | total bytes | alert | vs raw |
+|---|---|---|---|
+| raw (ship every event) | 99,122,250 (99 MB) | fired ✓ | 1.0× |
+| **distributed** | 14,747,280 (14.7 MB) | fired ✓ | **0.15× (6.7× less)** |
+| **geometric** | 13,033,555 (13.0 MB, 40/80 ships) | fired ✓ | **0.13× (7.6× less)** |
+
+**On real data every claim holds, and cleanly:** the sketch beats raw **6.7×**
+(distributed) / **7.6×** (geometric) — the cardinality is real (`H=5493`), so
+the fixed `d·w` cost is far below the 4M-event raw stream; and **geometric beats
+distributed** (13.0 vs 14.7 MB, ships 40/80) because a properly-sized `w=4096`
+sketch is sparse (no dense-broadcast amplification). The geometric margin is
+modest here because DEBS `F2` is a monotone all-day ramp (the sketch keeps
+drifting past the safe zone, so it ships ~half the windows); a stable/low-drift
+period — the scenario geometric targets — silences more and widens the gap. This
+is the honest end state of the whole thread: on a real workload, sketch ≫ raw and
+geometric > distributed, both by construction rather than a tuned synthetic.
+
+### Effect of the `C_ref` delta broadcast (design §12 open-problem #1)
+
+The geometric coordinator→edge `C_ref` was originally a full `~11.5 KB` matrix
+per resync × `k` edges (O(k) amplification), which made geometric *lose* the ramp
+regime. Delta-encoding the broadcast (sparse changed cells only):
+
+| ramp geometric | egress (`bytes_out`) | total |
+|---|---|---|
+| full-matrix broadcast | 1,107,936 | 1,620,000 (loses) |
+| **sparse delta broadcast** | **207,984** | **531,132 (wins)** |
+
+Egress dropped **5.3×**, flipping geometric from a loss to a `1.74×` win.
+
+### Delta-loss resilience (safety under a dropped `C_ref` delta)
+
+The sparse delta is a dependency chain, so a lost/corrupt `ΔC_ref` could leave an
+edge running its safe-zone test against a diverged reference — a *silent missed
+violation*. Reproduce: `deploy/mvp-multinode/scripts/f2_deltaloss_demo.sh`
+(injects loss on edge-0's 2nd delta via the f2driver `F2_INJECT` knob), same
+ramp/geometric scenario three ways:
+
+| scenario | alert | total bytes | ref_errs | behavior |
+|---|---|---|---|---|
+| no loss | **1** ✓ | 531,132 | 0 | baseline |
+| corrupt delta | **1** ✓ | 531,132 | 1 | edge-0 **detects** the bad delta → `needFull` → force-ship |
+| dropped delta | **1** ✓ | 506,106 | 0 | silent loss undetected by that edge; recovered by the periodic keyframe + the other edges' true sketches in the global merge |
+
+**The alert fires in all three cases** — safety is preserved under delta loss. A
+*corrupt* delta is caught at the edge (`ref_errs=1`, force-ship); a *silently
+dropped* delta has no sequence gap for that edge to detect (`ref_errs=0`), so its
+recovery rests on the coordinator's periodic Full keyframe (`F2_KEYFRAME_INTERVAL`)
+and the fact that the global `mean_f2` still sums the other edges' exact sketches.
+A per-broadcast sequence number (edge-detected gap → on-demand resync request)
+would close the silent-drop detection gap; it is noted as future work.
+
+---
+
+## 3. Woodruff–Zhang `k/ε²` reference
+
+WZ (STOC'12) proves continuous `(1±ε)` F₂ monitoring over `k` sites needs
+`Θ̃(k/ε²)` communication — a **hard lower bound** for any protocol. Normalizing by
+the "one round" unit `k·S` (`S ∝ d·w ∝ 1/ε²` = one sketch), here
+`k·S = 4 × 11,541 ≈ 46 KB`:
+
+| Mode / workload | total / (k·S) | reading |
+|---|---|---|
+| distributed | **20×** | ships every step → `W=20` rounds |
+| geometric, ramp | 11.5× | near-threshold ships |
+| geometric, stable | **5.0×** | within a small constant of the `k/ε²` floor |
+
+Distributed pays the full `W×` (re-ships every step); geometric approaches the
+`Θ̃(k/ε²)` floor in the stable regime (few resyncs). No protocol beats `k/ε²`
+worst-case — geometric's win is the data-dependent constant, exactly as the
+theory predicts.
+
+---
+
+## 4. Per-row sampling (unbiasedness)
+
+`CountSketch.UpdateStringSampledPerRow` (per-row geometric admission, `1/p`
+weighting): inserting a heavy key `N=20,000` times at `p=0.5` estimates within
+`<15%` relative error (`sketchlib-go .../sampled_test.go`), confirming the
+inverse-probability weighting keeps the estimator unbiased under per-row
+admission. Per §3.2 the per-row (vs per-item) form additionally decorrelates the
+`d` row estimates so the median concentrates the sampling error into the `(1−δ)`
+guarantee — realized at equal edge CPU.
diff --git a/docs/mvp-demo-runbook.md b/docs/mvp-demo-runbook.md
index 98e40cb6d..042bb088c 100644
--- a/docs/mvp-demo-runbook.md
+++ b/docs/mvp-demo-runbook.md
@@ -837,4 +837,4 @@ docker builder prune --all
 - `docs/comparison-asap-vs-databricks-pantheon-hydra.md` — architectural framing vs. Databricks Pantheon + Hydra
 - `docs/control-plane-design.md` — controller pipeline (L1 → L5)
 - `docs/e2e-test-guide.md` — pytest-style smoke tests (smaller scope than the MVP demo)
-- `docs/eval-instrumentation-notes.md` — measurement methodology notes
+- `docs/evaluation-plan-figures.md` — measurement methodology notes
diff --git a/docs/phase-2.md b/docs/phase-2.md
new file mode 100644
index 000000000..95a11e06b
--- /dev/null
+++ b/docs/phase-2.md
@@ -0,0 +1,891 @@
+# Phase 2 — history (extraction plan + performance audit)
+
+> **Historical record.** Consolidates the three Phase-2 docs (runtime
+> extraction map, Go perf audit, perf deployment) into one archive.
+> These record COMPLETED work; kept for provenance.
+
+
+---
+
+<!-- was: docs/phase-2-execution-plan.md — Runtime-extraction execution plan (ADR-0002 file-by-file map) -->
+
+## Phase 2 execution plan — extract `asap-precompute-go`
+
+_Companion to [ADR-0002](adr/adr-0002-extract-precompute-runtime.md).
+File-by-file extraction map for moving the runtime out of the
+five Go OTel processors into a shared `asap-precompute-go`
+module._
+
+## Inventory of source files
+
+```
+opentelemetry-collector-contrib-patch/processor/
+├── ddsketchprocessor/processor.go          942 LoC
+├── kllprocessor/processor.go                720 LoC
+├── hllprocessor/processor.go                785 LoC
+├── countsketchprocessor/processor.go        663 LoC
+└── countminsketchprocessor/processor.go     739 LoC
+                                          --------
+                                            3849 LoC total
+```
+
+The processors fall into two structural patterns that need to be
+harmonized in the extracted runtime:
+
+- **Pattern A** (DDSketch / KLL / HLL): nested
+  `resourceWindow → scopeWindow → metricWindow → sketchSeries`
+  hierarchy. Per-`pmetric.ScopeMetrics` aggregation. Explicit
+  `accumulateIntoWindow` / `flushWindow` pair.
+- **Pattern B** (CountSketch / CountMin): flat
+  `windowSketch` (map: partitionKey → sketch). Per-metric
+  aggregation. Timer-driven `startWindowLoop` /
+  `emitWindowAndReset`.
+
+Phase 2 unifies both into the generic `Precompute[SketchT]`
+shape from ADR-0002. The window manager picks
+tumbling/sliding/batch internally based on `PrecomputeConfig`.
+
+## Target layout
+
+```
+asap-precompute-go/
+├── go.mod                     // module github.com/ProjectASAP/asap-precompute-go
+├── observation.go              // Observation + ObservationValue (~50 LoC)
+├── envelope.go                 // SketchEnvelope view of the sketchlib-go proto (~30 LoC)
+├── precompute.go               // Precompute interface + generic impl (~250 LoC)
+├── window.go                   // tumbling / sliding / batch logic (~250 LoC)
+├── snapshot_cache.go           // outbound + inbound caches; ComputeDelta (~200 LoC)
+├── matchers.go                 // LabelMatcher, seriesKey, seriesAttrs (~150 LoC)
+├── config.go                   // PrecomputeConfig, AggregationMode, OnOverflow (~120 LoC)
+├── adapter.go                  // Adapter interface + Decode/Encode helpers (~100 LoC)
+├── controlchannel/
+│   ├── channel.go              // ControlChannel interface (~30 LoC)
+│   ├── http_poll.go            // HttpPollChannel impl (~80 LoC)
+│   └── opamp.go                // OpAmpChannel adapter wrapping existing controller/opamp (~50 LoC)
+├── telemetry.go                // PrecomputeStats + recordInput/recordOutput (~80 LoC)
+└── otel/                       // OTel-flavored Adapter helpers (consumed by Phase-2 shims)
+    ├── decode.go               // pmetric.Metrics → []Observation
+    ├── encode.go               // []SketchEnvelope → pmetric.Metrics
+    └── seriesattrs.go          // attribute key construction
+```
+
+Approximate total: 1500–1800 LoC. Each existing OTel processor
+shrinks to ~50–80 LoC shim that constructs a
+`Precompute[<sketch type>]` and delegates `ConsumeMetrics`.
+
+## Function-level extraction map
+
+For each existing function, where it goes after Phase 2:
+
+### Pattern A (DDSketch, KLL, HLL) — same map applies to all three
+
+| Today | Layer | Becomes |
+|---|---|---|
+| `type resourceWindow / scopeWindow / metricWindow / sketchSeries` | 3 | `precompute.go` — collapse into a single `series` struct keyed by `(agg_id, label_key)` since the resource/scope hierarchy was an OTel-side concern, not an algorithmic concern. |
+| `func newProcessor` | 4 | stays in shim; constructs `Precompute[*ddsketch.DDSketch]` |
+| `func Start` | 4 | stays in shim; spawns ticker goroutine that calls `Precompute.Tick` and emits via `Adapter.Encode` + `next.ConsumeMetrics` |
+| `func Shutdown` | 4 | stays in shim; cancels ticker, calls `Precompute.Shutdown` |
+| `func ConsumeMetrics` | 4 | stays in shim; calls `Adapter.Decode(md)` → `for _, o := range obs { p.pc.Observe(o) }` → `next.ConsumeMetrics(ctx, md)` (pass-through) |
+| `func processBatch / processScopeMetrics` | 4 | becomes the `otel.Decode` helper; produces `[]Observation` |
+| `func consumeDDSketchDataPoints / consumeGaugeDataPoints` | 4 | folded into `otel.Decode`; produces `Observation::Envelope` for sketch-typed inputs and `Observation::Float` for scalar |
+| `func decodeDDSketchDataPoint` | 4 | folded into `otel.Decode` envelope path |
+| `func cacheInboundSnapshot` | 3 | `snapshot_cache.go::CacheInbound` |
+| `func newSketchSeries / updateWindow / merge / ensureSketch` | 3 | `window.go` window-state helpers |
+| `func serializeDDSketch` | 1 | already lives in `sketchlib-go`; called via `Sketch.Snapshot()` |
+| `func computeDDSketchDelta` | 3 | `snapshot_cache.go::ComputeDelta` |
+| `func attributesKey / seriesKey / seriesAttrs / matchesMatchers / newSeriesFrom` | 3 | `matchers.go::SeriesKey / SeriesAttrs / Matches / NewSeries` |
+| `func accumulateIntoWindow` | 3 | `window.go::Observe` (merged with `Precompute::Observe`) |
+| `func getOrCreateMetricWindow` | 3 | private to `window.go` |
+| `func accumulateGaugeMetric / accumulate{DD,KLL,HLL}SketchMetric` | 3+4 | sketch-specific `Sketch::Observe` lives at L1; the routing logic (raw vs envelope) is in `Precompute::Observe` |
+| `func flushWindow` | 3 | `Precompute::Tick`; emits `[]SketchEnvelope` |
+| `func buildMetric / buildMergedSketchMetric / buildQuantileMetric` | 4 | becomes `otel.Encode`; produces `pmetric.Metrics` from `[]SketchEnvelope` |
+| `func enableSelfMonitoring / shutdownMonitor` | 4 | stays in shim |
+| `func recordInput / recordOutput / activeSeriesCount` | 3 | `telemetry.go` |
+
+### Pattern B (CountSketch, CountMin) — same map
+
+| Today | Layer | Becomes |
+|---|---|---|
+| `type windowSketch` | 3 | absorbed into `series` struct in `precompute.go` (one map: `(agg_id, label_key) → SketchT`) |
+| `func newProcessor / Start / Shutdown / Capabilities / ConsumeMetrics` | 4 | shim |
+| `func processMetrics / consumeBatch / ingestMetric / dpValue` | 4 | `otel.Decode`; produces `[]Observation` |
+| `func matchesMatchers / encodeKey / seriesKey / seriesAttrs / buildPartitionKey / encodeAttributesAsKey` | 3 | `matchers.go` |
+| `func accumulateIntoWindow / updateWindowSketch / mergeWindowSketch` | 3 | `Precompute::Observe + window.go` |
+| `func startWindowLoop / emitWindowAndReset / buildWindowMetricsAndReset` | 3+4 | timer goroutine moves to `Precompute` (driven by `Adapter::ScheduleTick`); `buildWindowMetricsAndReset` becomes `otel.Encode` |
+| `func inboundDecode{CS,CMS} / mergeWindow{CS,CMS}` | 3 | `snapshot_cache.go::ApplyDelta` (envelope-in path on `Precompute::ObserveEnvelope`) |
+| `func serialize{CountSketch,CMS} / deserialize{CMS} / clone{CS,CMS}` | 1 | already `sketchlib-go` API |
+| `func newConfiguredCountSketch / nextPowerOfTwo` | 4 | stays in shim (it's `Config` validation) |
+| `func recordInput / recordOutput / activeSeriesCount` | 3 | `telemetry.go` |
+
+### Cross-cutting
+
+- All five processors have an `enableSelfMonitoring` /
+  `shutdownMonitor` pair that emits OTel-shaped runtime metrics.
+  These become two pieces:
+  - `telemetry.go::PrecomputeStats` (host-neutral counters in
+    L3) — incremented from inside `Precompute`.
+  - The shim's `enableSelfMonitoring` reads `PrecomputeStats`
+    via `Adapter::EmitTelemetry` and constructs the OTel-shaped
+    metrics.
+
+## Per-processor shim shape (post-Phase-2)
+
+Every existing processor file becomes ~50-80 LoC of this shape:
+
+```go
+package ddsketchprocessor
+
+import (
+    precompute "github.com/ProjectASAP/asap-precompute-go"
+    otelhost  "github.com/ProjectASAP/asap-precompute-go/otel"
+    "github.com/ProjectASAP/asap-precompute-go/controlchannel"
+
+    "go.opentelemetry.io/collector/component"
+    "go.opentelemetry.io/collector/consumer"
+    "go.opentelemetry.io/collector/pdata/pmetric"
+)
+
+type ddsketchProcessor struct {
+    pc       precompute.Precompute  // generic over the sketch type bound by Config.SketchType
+    adapter  *otelhost.Adapter
+    cc       controlchannel.ControlChannel
+    cfg      *Config
+    next     consumer.Metrics
+    logger   *zap.Logger
+    monitor  *otelhost.SelfMonitor   // wraps PrecomputeStats
+    shutdown chan struct{}
+}
+
+func (p *ddsketchProcessor) Capabilities() consumer.Capabilities {
+    return consumer.Capabilities{MutatesData: false}
+}
+
+func (p *ddsketchProcessor) Start(ctx context.Context, host component.Host) error {
+    if err := p.pc.Start(ctx); err != nil { return err }
+    go p.controlChannelLoop(ctx)
+    go p.tickLoop(ctx)
+    return p.monitor.Start(ctx, host)
+}
+
+func (p *ddsketchProcessor) Shutdown(ctx context.Context) error {
+    close(p.shutdown)
+    return p.pc.Shutdown(ctx)
+}
+
+func (p *ddsketchProcessor) ConsumeMetrics(ctx context.Context, md pmetric.Metrics) error {
+    obs, err := p.adapter.Decode(md)
+    if err != nil { return err }
+    for _, o := range obs {
+        if err := p.pc.Observe(&o); err != nil { /* OnOverflow */ }
+    }
+    return p.next.ConsumeMetrics(ctx, md)
+}
+
+func (p *ddsketchProcessor) tickLoop(ctx context.Context) {
+    t := time.NewTicker(p.cfg.WindowSize)
+    defer t.Stop()
+    for {
+        select {
+        case <-p.shutdown: return
+        case now := <-t.C:
+            envelopes := p.pc.Tick(now.UnixMilli())
+            md := p.adapter.Encode(envelopes)
+            if err := p.next.ConsumeMetrics(ctx, md); err != nil { /* log */ }
+        }
+    }
+}
+
+func (p *ddsketchProcessor) controlChannelLoop(ctx context.Context) {
+    t := time.NewTicker(p.cfg.ControlPollInterval)
+    defer t.Stop()
+    for {
+        select {
+        case <-p.shutdown: return
+        case <-t.C:
+            if cs := p.cc.Poll(); cs != nil {
+                p.pc.UpdateConfig(cs)  // atomic swap inside Precompute
+            }
+        }
+    }
+}
+```
+
+The five processors share this scaffolding; the only per-processor
+differences are:
+
+- The generic `Precompute` type parameter (`*ddsketch.DDSketch`
+  vs `*kll.KllSketch` vs ...).
+- The `otel.Adapter` decode path — which `Metric.data` oneOf
+  variants it recognizes (DDSketch / KLLSketch / HLLSketch /
+  CountSketch / CountMinSketch).
+- Default config values (`alpha`, `k`, `precision`, `width`,
+  `depth`).
+
+A future refactor could collapse all five files into a single
+generic shim parameterized by sketch type. Phase 2 keeps them
+separate for OCB build-config compatibility (`builder-config.yaml`
+references each processor's package path).
+
+### Public test-friendly methods on the shim
+
+Today's tests directly call private methods that the shim model
+would otherwise hide. To avoid rewriting all 5 processor test
+files or adding incompatible private wrappers, the shim
+**promotes these to public methods** (per ADR-0002):
+
+```go
+// ProcessBatch decodes input, observes into Precompute, ticks
+// once (batch flushes per input batch), encodes envelopes, and
+// returns the synthesized output. Does NOT touch nextConsumer.
+// Useful as a test-friendly hook; production callers should use
+// ConsumeMetrics, which routes through the same pipeline plus
+// the downstream forwarding.
+func (p *ddsketchProcessor) ProcessBatch(ctx context.Context, md pmetric.Metrics) (pmetric.Metrics, error)
+
+// ProcessMetrics is the CountSketch / CMS naming variant of
+// ProcessBatch — same semantics, different historical name.
+func (p *countSketchProcessor) ProcessMetrics(ctx context.Context, md pmetric.Metrics) (pmetric.Metrics, error)
+
+// FlushWindow forces a tick on the precompute runtime and forwards
+// the synthesized output via nextConsumer.ConsumeMetrics. No-op
+// if no closed windows have data.
+func (p *ddsketchProcessor) FlushWindow(ctx context.Context) error
+```
+
+Tests adapt by capitalizing the method name (`processBatch` →
+`ProcessBatch`, etc.) — sed-style rename, no logic changes. This
+keeps `Capabilities() = {MutatesData: false}` honest because
+`ProcessBatch` returns a fresh `pmetric.Metrics` rather than
+mutating input md in place.
+
+## Phase 2 work breakdown
+
+Sequenced for incremental verification — each step is shippable
+and reversible:
+
+| Step | Scope | Verification |
+|---|---|---|
+| **2.1** Bootstrap `asap-precompute-go` module + types | `observation.go`, `envelope.go`, `config.go`, `adapter.go` (interface only). No logic, just type definitions matching ADR-0002 contracts. | `go vet ./...` clean. |
+| **2.2** Implement `matchers.go` and `snapshot_cache.go` | Move pure-data-structure logic with no host coupling: `LabelMatcher`, `SeriesKey`, snapshot cache, `ComputeDelta`. | Unit tests against fixed input vectors copied from existing processor tests. |
+| **2.3** Implement `window.go` and `precompute.go` | Generic Precompute implementation. Drives matchers + snapshot cache. Generic over `Sketch` interface. | Unit tests with mock Sketch (table-driven; verify tumbling, sliding, late-data, max_series, OnOverflow). |
+| **2.4** Implement `otel/{decode,encode}.go` | Pmetric decoders for DDSketch + Gauge + Sum. Encoders for `Metric.data = DDSketch{...}`. | Unit tests with fixed `pmetric.Metrics` fixtures. |
+| **2.5** Refactor `ddsketchprocessor` to shim | Delete the in-file accumulateIntoWindow/flushWindow/snapshot logic; replace with the shim shape above. Existing tests must still pass. | b3-delta e2e: same `19.49` value at offset −90s. |
+| **2.6** Refactor `kllprocessor` | Same as 2.5 but for KLL. | Existing KLL accuracy tests pass. |
+| **2.7** Refactor `hllprocessor` | Same as 2.5 but for HLL. | Existing HLL accuracy tests pass. |
+| **2.8** Refactor `countsketchprocessor` | Pattern B — verify flat-window collapse to (agg_id, label_key) keying preserves behavior. | CountSketch top-K accuracy reducer (`P8`) matches pre-extraction. |
+| **2.9** Refactor `countminsketchprocessor` | Same as 2.8 for CMS. | P8 accuracy reducer matches. |
+| **2.10** `controlchannel/http_poll.go` + adapter wiring | First non-OpAMP control channel. Backward-compat: existing OpAMP-driven deploys keep using `OpAmpChannel`; new deploys can opt into `HttpPollChannel`. | b3-delta e2e survives a runtime config push (sketch_type unchanged, window_size changed) without state loss. |
+| **2.11** Performance gate | Per-observation latency p99 within 10% of pre-refactor (R2). | Bench against the existing otel-app throughput harness. |
+
+Steps 2.1–2.4 can run in parallel (no inter-dependencies).
+Steps 2.5–2.9 are sequential (each builds on the verified shim
+shape from the previous). 2.10–2.11 gate the phase exit.
+
+## Risks during the migration
+
+- **R-Phase-2-A: Pattern-B → unified series-map collapse hides
+  partition semantics.** CountSketch / CMS today aggregate by
+  `partitionKey` (CS) or `aggregationKey` (CMS); these are
+  string concatenations of `(metric_name, label_subset)`. The
+  unified `(agg_id, label_key)` schema must preserve the same
+  string. Mitigation: in 2.8 / 2.9, write a key-equivalence test
+  before refactoring.
+- **R-Phase-2-B: Tick goroutine races with ConsumeMetrics.** Today
+  each processor uses an internal mutex. The extracted
+  `Precompute` must keep the same locking discipline (per-series
+  rwmutex; coarse global mutex around tick swap). Mitigation:
+  keep mutex shape identical in 2.3; race detector run on
+  refactored shims.
+- **R-Phase-2-C: OCB build manifest drift.** ASAPCollector's
+  `builder-config.yaml` references each processor's Go package
+  path. After Phase 2, those paths still work (the processor
+  packages still exist; they just call a new module). The new
+  `asap-precompute-go` module needs to be added to OCB's `gomod`
+  list. Mitigation: 2.5 includes `build_asap_otel.sh`
+  smoke run before merge.
+
+## Phase exit criterion (blocking)
+
+1. All five OTel processors are ≤80 LoC each (excluding factory
+   / config-validation boilerplate).
+2. b3-delta e2e produces the observed `19.49` value at offset
+   −90s, identical to pre-extraction.
+3. Per-observation `Observe` latency p99 within 10% of
+   pre-refactor.
+4. P8 accuracy reducer per-row error matches pre-extraction
+   for all five sketch types.
+5. `cargo test` / `go test` clean across all touched packages.
+6. `controlchannel.HttpPollChannel` smoke test: collector
+   running with the new control channel survives a controller
+   plan push that changes window size (without losing sketch
+   state mid-window).
+
+If any of (1)–(6) fails, Phase 2 does not merge.
+
+
+---
+
+<!-- was: docs/phase-2-perf-bench-go.md — Phase 2.11 path-A Go-side performance audit results -->
+
+## Phase 2.11 — Go benchmarks: pre-shim vs post-shim `Precompute.Observe`
+
+This doc records the results of the Phase 2.11 path-A Go-side
+performance audit. The 5 shim PRs (#226–#230) extracted the
+windowing, snapshot, and delta-encoding runtime out of the
+per-processor Go code into the host-neutral `asap-precompute-go`
+runtime. ADR-0002 §"Performance contract" pins a 10% gate on
+per-observation `Observe` latency at p99: post-shim must stay
+within 10% of pre-shim.
+
+The `testing.B` benchmarks in `asap-precompute-go/precompute_bench_test.go`
+exercise the exact code path the shim runs under load — `Precompute.Observe(*Observation)`
+— with realistic inputs for each of the five sketch types (DDSketch,
+KLL, HLL, CountSketch, CountMinSketch). The shim-side benchmarks in
+each `processor/<sketch>processor/processor_bench_test.go` capture
+absolute shim overhead (ProcessMetrics / ProcessBatch latency on a
+1000-data-point batch); these have no pre-shim equivalent because
+the legacy code wasn't a shim, so they're informational only.
+
+## Methodology
+
+### Hardware / toolchain
+
+- CPU: AMD Ryzen Threadripper PRO 5955WX 16-Cores
+- OS: Linux 5.15 (Ubuntu 20.04 kernel)
+- Go: `go1.25.3 linux/amd64`
+- Statistical comparison: `golang.org/x/perf/cmd/benchstat`
+
+### Commits
+
+- Pre-shim baseline: `6b3258d` (`test(integration/parity): all-sketch e2e parity harness (#225)`)
+  — last commit before the 5 shim PRs landed.
+- Post-shim head: `f9824e2` (`fix(asap-precompute-go): SnapshotCache always-refresh + extract common sketch wrappers (#232)`).
+
+### Bench-file portability
+
+`precompute_bench_test.go` is structured to compile against BOTH
+commits. It does NOT depend on the post-shim-only
+`asap-precompute-go/sketches/` wrapper subpackage; instead each
+benchmark wires a tiny `benchXxxWrapper` directly against
+`sketchlib-go` and an inline `benchXxxObserver` that satisfies
+`precompute.SketchObserver`. The wrappers implement only the
+methods `Observe` needs (no Snapshot / Merge / etc.) so the file
+applies cleanly onto pre-shim 6b3258d as well — pinning the
+measured code to the runtime's `Observe` path itself, independent
+of the sketches/ wrapper layer that didn't exist pre-shim.
+
+### Commands
+
+asap-precompute-go (run on each commit after applying the bench file):
+
+```
+cd asap-precompute-go
+# pre-shim (after `git checkout 6b3258d`):
+go test -bench=. -benchmem -count=5 -run=^$ . > /tmp/asap-pre.txt 2>&1
+# post-shim (after `git checkout phase2/perf-bench-go`):
+go test -bench=. -benchmem -count=5 -run=^$ . > /tmp/asap-post.txt 2>&1
+benchstat /tmp/asap-pre.txt /tmp/asap-post.txt
+```
+
+Per-processor shim benchmarks (post-shim only):
+
+```
+for p in ddsketch kll hll countsketch countminsketch; do
+  cd opentelemetry-collector-contrib-patch/processor/${p}processor
+  go test -bench=. -benchmem -count=5 -run=^$ . > /tmp/${p}-shim.txt 2>&1
+done
+```
+
+### Choices that affect the numbers
+
+- **Window size = 1 hour.** The bench loop runs millions of
+  iterations against a single Precompute instance; a 1h window
+  guarantees no rotation contaminates the per-observation timing.
+- **No `b.RunParallel`.** `Precompute` is mutex-guarded internally
+  (window + snapshot cache), so parallel benchmarks would measure
+  contention more than the per-call cost. Single-goroutine bench
+  matches what the shim does on a single ConsumeMetrics call.
+- **`b.ReportAllocs()`** on every bench so allocation regressions
+  surface alongside ns/op.
+- **Deterministic inputs.** Each bench uses
+  `rand.New(rand.NewSource(0x5A9C011EC709072))` so successive
+  runs are comparable.
+
+## `asap-precompute-go::Observe` results (the 10% gate)
+
+Five samples per benchmark, median reported. Full benchstat output
+in /tmp/asap-{pre,post}.txt — quoted in §"Raw benchstat" below.
+
+| Sketch | Pre (ns/op) | Post (ns/op) | Δ% | Gate |
+|---|---:|---:|---:|---|
+| DDSketch    | 158.10 | 155.80 | −1.45% | **PASS** |
+| KLL         | 288.40 | 290.60 | +0.76% | **PASS** |
+| HLL         | 146.60 | 147.70 | +0.75% | **PASS** |
+| CountSketch | 241.00 | 240.50 | −0.21% | **PASS** |
+| CountMinSketch | 345.20 | 351.90 | +1.94% | **PASS** |
+
+Allocations are bit-identical pre vs post for every sketch (DDSketch:
+24B/2 allocs, KLL: 81B/4, HLL: 24B/2, CountSketch: 32B/3, CMS:
+128B/5) — the shim refactor did not introduce allocation regressions
+on the hot path.
+
+**Verdict: all 5 sketches PASS the ADR-0002 §"Performance contract"
+10% gate.** The largest delta is +1.94% on CMS; the smallest is
+−1.45% on DDSketch (which is faster post-shim, consistent with
+benchmark noise, not a real speedup). benchstat's two-sample test
+flagged none of the deltas as statistically significant (every
+p-value > 0.2 with n=5 samples), which is itself noteworthy: the
+shim refactor is functionally a behavior-preserving move, and the
+benchmark numbers confirm that.
+
+## Per-processor shim results (post-shim only)
+
+These benchmarks measure the absolute cost of one
+`ProcessMetrics` / `ProcessBatch` call on a 1000-data-point
+synthetic batch. There is no pre-shim equivalent because the legacy
+code was not a shim — the per-processor `processor.go` files
+contained the runtime inline. Treat these as a baseline to detect
+future regression in the shim layer itself.
+
+Median of 5 samples, post-shim only:
+
+| Processor | Bench | ns/op | B/op | allocs/op |
+|---|---|---:|---:|---:|
+| ddsketch    | ProcessMetrics (window mode, observe-only) | 552,704 | 355,116 | 5,002 |
+| ddsketch    | ProcessBatch  (batch mode, observe + tick + encode) | 867,072 | 572,606 | 10,167 |
+| kll         | ProcessBatch | 737,000 | 450,199 | 10,113 |
+| hll         | ProcessBatch | 783,714 | 798,082 | 7,202 |
+| countsketch | ProcessMetrics (batch mode) | 1,209,997 | 711,312 | 10,160 |
+| countminsketch | ProcessBatch | 2,158,571 | 2,108,229 | 18,312 |
+
+Notes:
+
+- The ddsketch shim is the only one that exposes a window-mode
+  observe-only path (`ProcessMetrics`) distinct from batch
+  (`ProcessBatch`); the other four shims fold tick + encode into
+  every public call. Comparing ddsketch's 552,704 ns/op
+  (observe-only) vs 867,072 ns/op (with tick + encode) gives a
+  rough sense of the encode-side overhead per 1000-point batch:
+  ~315k ns, dominated by serialization + pmetric construction
+  not the runtime itself.
+- CMS's 2.1ms / 1000-point batch is the slowest of the five and
+  is bounded by `common.FromBytes(...).Hash` cost on every
+  observation (the legacy CMS shim does the same hash in the
+  same place; this is sketchlib-go's hash, not new shim
+  overhead).
+
+## Raw benchstat
+
+```
+$ benchstat /tmp/asap-pre.txt /tmp/asap-post.txt
+goos: linux
+goarch: amd64
+pkg: github.com/ProjectASAP/asap-precompute-go
+cpu: AMD Ryzen Threadripper PRO 5955WX 16-Cores
+                                  │ /tmp/asap-pre.txt │         /tmp/asap-post.txt         │
+                                  │      sec/op       │    sec/op     vs base              │
+Precompute_Observe_DDSketch-32           158.1n ± ∞ ¹   155.8n ± ∞ ¹       ~ (p=0.651 n=5)
+Precompute_Observe_KLL-32                288.4n ± ∞ ¹   290.6n ± ∞ ¹       ~ (p=0.222 n=5)
+Precompute_Observe_HLL-32                146.6n ± ∞ ¹   147.7n ± ∞ ¹       ~ (p=0.460 n=5)
+Precompute_Observe_CountSketch-32        241.0n ± ∞ ¹   240.5n ± ∞ ¹       ~ (p=0.548 n=5)
+Precompute_Observe_CMS-32                345.2n ± ∞ ¹   351.9n ± ∞ ¹       ~ (p=1.000 n=5)
+geomean                                  223.4n         224.2n        +0.35%
+¹ need >= 6 samples for confidence interval at level 0.95
+```
+
+(B/op and allocs/op tables omitted — every cell is byte-identical
+between pre and post, geomean Δ = 0.00%.)
+
+## Caveats
+
+- `benchstat` flagged "need >= 6 samples for confidence interval"
+  on every row. We ran with `-count=5` per the task brief; the
+  geomean drift of +0.35% is well inside what `count=20` would
+  surface as noise, but a deeper run is left to a follow-up
+  audit if the controller surface ever needs to defend a tighter
+  bound.
+- The bench file lives in package `precompute_test` (external
+  test package) and uses tiny inline wrappers around sketchlib-go
+  rather than the post-shim `sketches/` package, so the same
+  source compiles on 6b3258d and HEAD. This is the only way to
+  apples-to-apples compare the runtime's `Observe` cost across
+  the commit boundary; the public `sketches/` wrappers are an
+  insignificant sliver of the call graph (one method dispatch +
+  one type assert), so the small wrapper-layer overhead they add
+  on HEAD is captured in the +0.76% / +1.94% post-shim drift the
+  table reports — comfortably under the 10% gate.
+- Running benchmarks with `-race` is excluded per the task brief
+  (and is the right call: race instrumentation distorts ns/op
+  by 5-50x).
+
+## Conclusion
+
+Five sketch types, five PASS verdicts. The shim refactor (PRs
+#226–#230) preserves per-observation latency to within ±2% on a
+deterministic single-machine benchmark, well inside ADR-0002's
+10% performance contract. No regression to investigate; nothing
+to escalate.
+
+
+---
+
+<!-- was: docs/phase-2-perf-deployment.md — Phase 2.11A performance deployment notes -->
+
+## Phase 2.11B — Deployment Performance: pre-shim vs post-shim
+
+Companion to `docs/phase-2-perf-bench-go.md` (Phase 2.11A, PR #236). The
+A path closed ADR-0002 §"Performance contract" at the
+microbenchmark level (`Precompute.Observe` ns/op). This doc reports the
+deployment-level confirmation: what the existing docker-compose
+b3-delta harness measures end-to-end, and whether those numbers move
+materially between commit `6b3258d` (pre-shim, last commit before the
+5 shim PRs landed) and `c86a62c` (post-shim, HEAD of `origin/main`).
+
+The aim is not a fresh measurement framework — it's a sanity check on
+the harness we already ship, so a future reader can see that the shim
+extraction (PRs #226–#232) didn't blow up the deployed agent's
+throughput, RSS, or per-window output bytes.
+
+## Setup
+
+### Hardware / toolchain
+
+- CPU: AMD Ryzen Threadripper PRO 5955WX (32 logical cores)
+- RAM: 440 GiB (essentially unconstrained for this stack)
+- OS: Linux 5.15 (Ubuntu 20.04 kernel)
+- Go: `go1.25.3 linux/amd64`
+- Docker: 28.1.1
+- Other tenants on the host: an Elasticsearch + Kibana dev stack
+  (idle, healthcheck-only). Not isolated, so absolute numbers carry
+  some noise.
+
+### Stack
+
+`deploy/mvp-singlenode/docker-compose/baseline-b3-delta.yml` over the shared `base.yml`
++ `agents-N1.yml` overlay. B3-delta is the "delta sketch transmission,
+60 s window" baseline; it's the same combination Phase 2.11A's micro
+results care about, since the shim sits in the agent processor pipeline
+that this baseline exercises.
+
+Workload knobs (defaults from `base.yml`):
+
+- `-freq-hz=10` — 10 Hz event rate from the synthetic producer
+- `-cardinality=1000` — 1000 active series
+- `-sdk-window=15s` — SDK aggregation window
+- `-agg=default` — Sum / LastValue per metric kind
+
+One agent (N1), one gateway, one backend. No load-gen client; the
+otel-app is the only writer.
+
+## Methodology
+
+The harness has two relevant scripts:
+
+- `deploy/mvp-singlenode/scripts/measure-baseline.py` — instant Prometheus query for
+  per-tier CPU / RSS / point rate / output bytes, plus a
+  `docker stats` two-sample window for backend + producer numbers
+  (script supplements Prom because cAdvisor isn't in the stack).
+- `deploy/mvp-singlenode/scripts/run-baseline-sweep.sh` — orchestrator that brings the
+  stack up, soaks for `SOAK_S` seconds, then invokes
+  `measure-baseline.py`. We do not use the sweep wrapper here because
+  the goal is one stack soak per commit, not the
+  baseline × rate × cardinality matrix.
+
+### Commit handling
+
+`6b3258d` predates PR #231 ("wire asap-precompute-go replace
+directive") but the pre-shim binary doesn't import `asap-precompute-go`
+at all (the shim-extraction PRs are precisely what introduced that
+dependency), so no local fix was needed for the OCB build to succeed.
+The only environmental fixup was a symlink `/tmp/sketchlib-go ->
+/home/zeying/repos/sketchlib-go`, because the OCB-emitted go.mod uses
+`../../../../sketchlib-go` from the build dir at
+`/tmp/preshim-worktree/.../cmd/asap-otel/`. Both fixups are
+build-host-local — nothing was committed.
+
+### Procedure
+
+For each commit:
+
+1. `git worktree add` at the commit, init submodules, run
+   `./build_asap_otel.sh` to produce a fresh
+   `asap-otel` binary.
+2. Copy that binary into the main repo's
+   `opentelemetry-collector-contrib-patch/cmd/asap-otel/` and
+   `docker build -f deploy/docker/Dockerfile.asap-otel`. Tag
+   appropriately, swap onto `:dev`, then
+   `docker compose ... up -d --force-recreate agent-1 gateway` so only
+   the agent + gateway tier get re-imaged. Producer / backend /
+   controller / Prom / MinIO stay continuously up, which removes a
+   source of cross-run drift.
+3. Soak ≥ 200 s (≥ 3 windows of the 60 s delta cycle, so
+   `rate(...[2m])` sees ≥ 2 samples — required by the harness).
+4. Take 2 readings ≥ 60 s apart with
+   `measure-baseline.py --window 2m --bytes-sample-window 10`; report
+   the mean of the two.
+
+## Results
+
+Two readings per commit. Numbers in the table are the **mean** of the
+two samples. Raw CSV in `/tmp/perf-2-11b/{preshim,postshim}.csv` on
+the build host.
+
+### Single-sample raw values
+
+```
+b3-delta-preshim,    agent_cpu=0.003 cores, agent_rss=288.3 MiB, agent_in=26.40 KiB/s, agent_out=4.69 KiB/s, agent_pts=133.3 /s
+b3-delta-preshim-2,  agent_cpu=0.002 cores, agent_rss=293.9 MiB, agent_in=25.08 KiB/s, agent_out=2.35 KiB/s, agent_pts=133.3 /s
+b3-delta-postshim,   agent_cpu=0.003 cores, agent_rss=302.6 MiB, agent_in=26.41 KiB/s, agent_out=4.70 KiB/s, agent_pts=133.3 /s
+b3-delta-postshim-2, agent_cpu=0.002 cores, agent_rss=302.6 MiB, agent_in=25.08 KiB/s, agent_out=2.35 KiB/s, agent_pts=133.3 /s
+```
+
+### Comparison table
+
+| Metric                  | Pre-shim (6b3258d) | Post-shim (c86a62c) | Δ (post − pre) | Δ %    | Verdict |
+|-------------------------|--------------------|---------------------|---------------:|-------:|---------|
+| agent_cpu_cores         | 0.0025             | 0.0025              |       +0.0000  |   0.0% | pass    |
+| agent_rss_mib           | 291.1              | 302.6               |        +11.5   |  +4.0% | pass    |
+| agent_in_kib_per_s      | 25.74              | 25.74               |        +0.00   |   0.0% | pass    |
+| agent_out_kib_per_s     | 3.52               | 3.52                |        +0.00   |   0.0% | pass    |
+| agent_points_per_s      | 133.3              | 133.3               |        +0.0    |   0.0% | pass    |
+
+Throughput, input bytes, output bytes, and CPU are essentially
+identical — the in/out/points columns match to three significant
+figures because the workload is producer-paced (10 Hz × 1000
+cardinality) and well below saturation; the agent is so far below
+its capacity that the shim's extra method-call hop doesn't show up
+as a CPU delta at all.
+
+The 4% RSS bump is the only directional change. It is consistent
+with the shim's explicit `Precompute` runtime structure (snapshot
+cache, per-source state map) being slightly fatter than the inlined
+processor state it replaced. ADR-0002 doesn't gate on RSS, but a
+4% bump on a 290 MiB agent footprint is well inside what would be
+considered a non-regression — the larger agent_rss drivers
+(sketchlib-go DDSketch buffers, OTel runtime) are roughly 10×
+larger.
+
+### Producer / backend rows (informational)
+
+| Metric                  | Pre-shim sample mean | Post-shim sample mean | Notes                                    |
+|-------------------------|----------------------|-----------------------|------------------------------------------|
+| producer_cpu_cores      | 0.061                | 0.071                 | producer container un-restarted; 4-h-old |
+| producer_rss_mib        | 56.9                 | 75.5                  | (same; runtime drift, not shim)          |
+| backend_cpu_pct         | 0.01                 | 0.94                  | backend never restarted                  |
+| backend_rss_mib         | 163.6                | 134.0                 | (same; GC noise, not shim)               |
+
+The producer + backend containers were intentionally **not** restarted
+between pre-shim and post-shim measurement — only the agent + gateway
+were re-imaged. So these rows compare two snapshots of the *same*
+running container hours apart, which is just the runtime's heap / GC
+drift over time. They are recorded for completeness but do not say
+anything about the shim. Counterintuitively, the post-shim
+`backend_rss_mib` is *lower* than pre-shim — that's because the
+post-shim row was captured first (after 4 h of soak), the pre-shim
+row 9 minutes later; RSS difference between two snapshots of the
+unchanged backend container is just GC-cycle noise.
+
+### Gateway + backend Prom rows: NaN
+
+`gateway_cpu_cores`, `gateway_rss_mib`, `gateway_points_per_s`,
+`gateway_out_series_per_s`, `backend_samples_per_s`,
+`backend_query_p99_ms` are all NaN in the CSV — see "Gaps" below.
+Same NaN pattern in pre-shim and post-shim, so the comparison still
+holds for the rows that do populate.
+
+## Verdict
+
+**Phase 2.11A** (PR #236) confirmed the per-observation gate at the
+microbenchmark level: pre vs post-shim `Precompute.Observe` p99 within
+the ADR-0002 10% tolerance for all five sketches.
+
+**Phase 2.11B** (this doc) confirms the deployment-level non-regression:
+on the b3-delta harness, every shim-affected metric — agent CPU, in /
+out KiB/s, throughput — is within run-to-run noise of pre-shim. RSS
+moves +4% which is well inside any reasonable tolerance and explained
+by the explicit shim runtime structure replacing inlined state.
+
+Together Phase 2.11A and 2.11B close ADR-0002 §"Performance contract"
+with both micro and deployment-level confirmation.
+
+### Caveats
+
+- **Single host, single-machine docker noise.** Two-sample mean for
+  each metric, but only one stack soak per commit. Run-to-run variance
+  in `agent_out_kib_per_s` is intrinsic to the 60 s delta window —
+  `rate()` over 2 m sees 2–3 emissions, so 30–50% jitter on that
+  column within a single stable run is normal (4.7 → 2.4 KiB/s
+  between samples 60 s apart, identical between commits).
+- **Shared host.** A separate Elasticsearch dev stack ran during
+  measurement (idle but resident); not isolated to a cgroup boundary.
+- **Producer-paced workload.** At 1000 cardinality × 10 Hz the agent
+  CPU is ~ 0.0025 cores — three orders of magnitude below saturation.
+  This deployment audit confirms there's no *new* overhead, but does
+  not stress the shim. A higher-cardinality stress test (e.g. 1e5
+  cardinality × 100 Hz) would be more discriminating; see "Gaps"
+  for why we don't run it here.
+- **No cold-store or query traffic.** This soak measured ingest only;
+  `backend_samples_per_s` and `backend_query_p99_ms` are NaN because
+  no PromQL replay client ran. The end-to-end query path is exercised
+  by `run_e2e_sweep.sh` which is much more expensive (5 sketch
+  families × 12 cells × ≥ 2 min each ≥ 2 h wall-clock) and out of
+  scope for this audit.
+- **No `-race`, no profiling overhead.** Plain release build via
+  `Dockerfile.asap-otel`.
+
+## Gaps in the existing harness
+
+The four gaps that came up while running the Phase 2.11B audit have
+been triaged below. Each is annotated with the resolution from PR
+#246 (`fix(perf-harness): close 4 gaps from Phase 2.11B deployment
+perf run`); two more harness gaps that surfaced separately are
+listed at the end as standing follow-ups.
+
+1. **Gateway metric-name skew — FIXED in PR #246.**
+   `measure-baseline.py` was written when the gateway was on otelcol
+   v0.108 (no `_total` suffix on process counters). The current
+   gateway image is v0.141 (matches the agent), so
+   `gateway_cpu_cores` / `gateway_rss_mib` / `gateway_points_per_s` /
+   `gateway_out_series_per_s` all returned NaN against today's
+   stack. Fix: each gateway query is now `<v0.141 name> or <v0.108
+   name>`, so the script keeps producing rows whether the gateway
+   image is current or a legacy worktree replay.
+
+2. **Backend `/metrics` is empty under ingest-only — DOCUMENTED in
+   PR #246, deferred as a design-level concern.** The backend's
+   `asap_ingest_samples_total` and `asap_query_duration_seconds_bucket`
+   only get populated when PromQL query traffic flows; an ingest-only
+   soak (this audit, `run-baseline-sweep.sh`'s default) leaves both
+   at NaN. This is *not* a query-string bug — the metrics genuinely
+   don't exist under ingest-only operation, so editing
+   `measure-baseline.py` won't help.
+
+   Closing the gap properly requires either:
+
+   - **Replay path on the harness side.** Add an opt-in MetricsQL
+     replay client (the existing `deploy/mvp-singlenode/scripts/metricsql_replay.py`
+     primitives are a starting point) that the sweep wrapper drives
+     before the measurement window. This is its own feature with
+     its own design questions (which queries to replay, at what
+     rate, on which sketch families) and is out of scope for a
+     harness-fixes PR.
+   - **Synthetic ingest-side counter on the backend.** The backend
+     could expose an `asap_ingest_envelopes_total` counter that
+     fires regardless of whether query traffic ran. That's a
+     backend code change, also out of scope for a Collector-side
+     harness PR.
+
+   Because the harness can't synthesize these metrics by itself,
+   PR #246 only updates the docstring on the `backend_samples_per_s`
+   / `backend_query_p99_ms` query templates to mark them as
+   "requires query traffic"; the operator now sees in-script why
+   the column is blank. The deeper "ingest-only vs ingest+query
+   soak" mode distinction is tracked as a follow-up item; it
+   belongs in a `run-baseline-sweep.sh` redesign, not a one-shot
+   query-template fix.
+
+3. **No per-observation latency emission from the deployed shim —
+   FIXED in PR #246 (DDSketch only) + follow-up.** ADR-0002's
+   binding metric is per-observation `Observe` p99, which the
+   deployed asap-otel previously didn't expose as a Prom
+   histogram (Phase 2.11A measured it in `testing.B` only).
+
+   Resolution:
+
+   - **Runtime.** `asap-precompute-go` now exposes a
+     `LatencyObserver func(d time.Duration)` hook installed via
+     `Precompute.SetLatencyObserver`. The hook fires once per
+     `Observe` call (success, ErrSeriesCapExceeded, ErrLateData,
+     and matcher-miss all time), giving the deployed shim the same
+     envelope `testing.B` measures. Nil-safe at the hot path
+     (atomic-pointer load + nil check).
+   - **DDSketch shim wiring.** `ddsketchprocessor.enableSelfMonitoring`
+     constructs a `Float64Histogram` named
+     `asap_processor_observe_seconds` with bucket boundaries
+     spanning 50 ns – 10 ms (covers the 80–500 ns/op post-shim
+     micro envelope plus tail). Each per-metric Precompute spawned
+     via `getOrCreate` picks up the histogram via
+     `proc.recordObserveLatency`. The histogram appears on the
+     gateway / agent `/metrics` endpoint when
+     `EnableSelfMonitoring=true` (the production default).
+   - **Other 4 shims (KLL, HLL, CountSketch, CountMin) — follow-up.**
+     The runtime change is fully backwards-compatible: shims that
+     don't call `SetLatencyObserver` lose nothing. Wiring the
+     histogram into the remaining four processors is a mechanical
+     copy of the DDSketch monitor.go diff; pulled out of this PR
+     to keep the diff focused per the PR-scope constraint. Tracked
+     as **Phase 2.11C**.
+
+4. **No direct sketch-payload-bytes metric — STANDING.**
+   `agent_out_kib_per_s` is the OTel-collector-level processor
+   output bytes, which conflates delta-encoded sketch payload bytes
+   with envelope metadata. The B3-delta savings claim requires
+   distinguishing the two; this is visible in
+   `gateway_out_series_per_s` minus a B0a (raw stream) reference,
+   but the delta isn't a single column. A
+   `asap_sketch_payload_bytes_per_window` counter on the processor
+   would close this gap. Not addressed in PR #246.
+
+5. **Legacy rate knob removed.** The old per-second rate knob was a
+   no-op under SDK aggregation and has been dropped entirely; the
+   workload is paced by `-freq-hz` and flushed by `-sdk-window`.
+   The sweeps no longer carry the dead dimension.
+
+6. **Producer-paced workload caps the discriminating power — FIXED
+   in PR #246.** At cardinality 1000 × 10 Hz the agent ran at
+   ~0.25% of one core so CPU diffs were dominated by measurement
+   noise. `baseline-b3-delta.yml` now overrides
+   `-cardinality` and `-freq-hz` to 1e5 × 100 Hz,
+   chosen to land the agent in the 50–70% one-core band on
+   reference hardware (Threadripper PRO 5955WX as described in
+   the "Hardware" section). The override still honours host-env
+   shadowing — set `OTELAPP_CARDINALITY=1000`
+   on the host to recover the legacy quiet profile for ad-hoc work.
+
+   Re-running the pre-shim vs post-shim comparison under the new
+   profile is its own measurement and is **not** included in this
+   PR; the PR only updates the harness so the next operator who
+   runs the sweep sees CPU-cores deltas instead of measurement
+   noise. The numerical re-baselining belongs in a Phase 2.11C
+   "saturating-load comparison" doc.
+
+## Reproduction
+
+The recipe used to produce the numbers above:
+
+```
+# build pre-shim binary in a worktree (sibling sketchlib-go must exist)
+git worktree add /tmp/preshim-worktree 6b3258d
+cd /tmp/preshim-worktree
+git submodule update --init --recursive opentelemetry-collector \
+  opentelemetry-collector-contrib opentelemetry-proto
+ln -sfn /home/zeying/repos/sketchlib-go /tmp/sketchlib-go
+GOPRIVATE='github.com/ProjectASAP/*' bash build_asap_otel.sh
+
+# build pre-shim docker image
+cp /tmp/preshim-worktree/opentelemetry-collector-contrib-patch/cmd/asap-otel/asap-otel \
+   $REPO/opentelemetry-collector-contrib-patch/cmd/asap-otel/
+cd $REPO
+docker build -f deploy/docker/Dockerfile.asap-otel -t asap/asap-otel:preshim .
+
+# swap onto :dev tag, recreate just agent + gateway, soak, measure
+docker tag asap/asap-otel:dev asap/asap-otel:postshim-saved
+docker tag asap/asap-otel:preshim asap/asap-otel:dev
+cd $REPO/deploy/docker-compose
+AGENT_CONFIG=asap-otel-agent-b3-delta.yaml docker compose \
+  -f base.yml -f agents-N1.yml -f baseline-b3-delta.yml \
+  up -d --no-deps --force-recreate agent-1 gateway
+sleep 200  # 2 m for rate window + 80 s margin
+python3 $REPO/deploy/mvp-singlenode/scripts/measure-baseline.py \
+  --baseline b3-delta-preshim --scale N1 --rate 1000 --cardinality 1000 \
+  --window 2m --bytes-sample-window 10
+sleep 60
+python3 $REPO/deploy/mvp-singlenode/scripts/measure-baseline.py \
+  --baseline b3-delta-preshim-2 --scale N1 --rate 1000 --cardinality 1000 \
+  --window 2m --bytes-sample-window 10
+
+# restore post-shim and re-measure (or just keep the prior post-shim numbers)
+docker tag asap/asap-otel:postshim-saved asap/asap-otel:dev
+docker compose ... up -d --no-deps --force-recreate agent-1 gateway
+# (etc.)
+```
diff --git a/docs/sampling-cdm-gos-derivations.md b/docs/sampling-cdm-gos-derivations.md
new file mode 100644
index 000000000..e3241c862
--- /dev/null
+++ b/docs/sampling-cdm-gos-derivations.md
@@ -0,0 +1,1695 @@
+# Sampling + Continuous Distributed Monitoring (CDM) / Geometric-OctoSketch (GOS) derivations for windowed sketch telemetry
+
+> Paper-facing derivation note. This document ties together
+> [distributed-nitrosketch-coordinated-sampling.md](distributed-nitrosketch-coordinated-sampling.md),
+> [continuous-monitoring-aggregation-taxonomy.md](continuous-monitoring-aggregation-taxonomy.md),
+> [continuous-monitoring-tumbling-cost-analysis.md](continuous-monitoring-tumbling-cost-analysis.md),
+> and [design-gos-unified-edge-telemetry.md](design-gos-unified-edge-telemetry.md).
+>
+> Scope: fixed tumbling-window epochs; open-window freshness inside one epoch;
+> additive linear sketch states as the fully proved case; family-specific
+> extensions for quantiles and cardinality sketches.
+>
+> Acronyms: **CDM** = **Continuous Distributed Monitoring**; **GOS** =
+> **Geometric-OctoSketch**.
+
+## Paper positioning: bottleneck -> solution map
+
+The system bottleneck addressed by these derivations is
+**resolution-coupled central ingestion**. In conventional observability
+pipelines, higher temporal resolution, higher label cardinality, and lower
+freshness latency all require more raw samples to traverse the central path:
+
+```text
+collector/exporter -> remote write / queue / WAL -> backend ingest/index/storage
+-> query scan
+```
+
+ASAPCollector changes the unit of work from raw-sample ingestion to
+query-bounded sketch-state synchronization. Edge collectors still absorb the
+high-resolution stream, but the backend receives bounded sketch summaries,
+error-triggered deltas, and optional cold raw fallback instead of every raw
+sample on the warm path.
+
+The problem-solution pairs are:
+
+| Existing-system bottleneck | Why it matters | ASAPCollector mechanism | Error/control consequence |
+| --- | --- | --- | --- |
+| Central ingest bottleneck | Cost scales with $\mathrm{series\_cardinality} \times \mathrm{sample\_frequency}$; raising resolution pushes more samples through write queues, indexing, storage, and query scan. | Maintain mergeable sketches at edge collectors and transmit sketch state/deltas. | Backend warm-path load scales with summary size and threshold crossings, not directly with raw sample rate. |
+| Freshness vs cost bottleneck | Shorter scrape/export intervals improve open-window freshness but increase CPU, network, and backend ingest pressure; longer intervals reduce cost but make queries and alerts stale. | Use CDM/GOS residual thresholds $T_j$ for error-triggered synchronization. | Freshness becomes a bounded staleness term $\mathrm{Err}_q^{cdm}$ rather than an implicit consequence of a fixed reporting interval. |
+| Query scan / post-ingest downsampling bottleneck | TSDB compression and downsampling help after raw data has already been ingested, and long-range queries still depend on stored sample layout. | Build query-ready sketches before/during ingestion. | Query cost is paid against sketch summaries, while the cold raw path remains available for unsupported or forensic queries. |
+| Fixed-statistics / early-binding bottleneck | Histograms, summaries, and pre-aggregations commit early to bucket layouts, quantiles, windows, or rollups; changing the query later may be impossible or inaccurate. | Use a sketch-family adapter per query class: Sum/CMS/CountSketch for additive readouts, DDSketch/KLL for rank queries, HLL for cardinality. | The controller applies the correct sampling and staleness model for each sketch family instead of using one proof for all summaries. |
+| Control-plane bottleneck | Existing cost controls such as dropping labels, filtering metrics, increasing intervals, or coarse downsampling often do not expose a query-level error budget. | Jointly tune sketch size, sampling probabilities $p_i$, and GOS thresholds $T_j$. | The system can allocate a query error budget across $\mathrm{Err}_q^{sk} + \mathrm{Err}_q^{sa} + \mathrm{Err}_q^{cdm}$. |
+| Theory-to-system bottleneck | Prior sketching, approximate query processing, and CDM work each solve part of the problem, but not the end-to-end telemetry ingestion control loop. | Combine update sampling, mergeable sketches, and CDM/GOS synchronization inside the collector/backend architecture. | The paper claim is a system-level accuracy envelope, not only a faster sketch update or a lower communication protocol in isolation. |
+
+Compared with existing work, the paper's positioning is:
+
+| Existing work / system family | Solves | Leaves open | ASAPCollector angle |
+| --- | --- | --- | --- |
+| Prometheus / OpenTelemetry / remote-write pipelines | Standard metric collection, export, and backend ingestion. | Higher resolution and cardinality still increase central ingest work. | Move high-resolution absorption to edge sketches and synchronize bounded state. |
+| Native histograms / exponential histograms / DDSketch-style distribution metrics | Compact, mergeable distribution summaries. | Mainly distribution-specific; does not give a general control plane for sampling plus staleness across sketch families. | Treat DDSketch as one adapter in a broader sketch taxonomy. |
+| TSDB compression / Thanos-style downsampling | Long-range query acceleration and storage-layout optimization. | Mostly after-ingest optimization; raw samples still enter the central path first. | Summarize before or during ingestion, then query the warm sketch path. |
+| BlinkDB / VerdictDB-style AQP | Approximate analytics over stored data with statistical error. | Data is already in the warehouse; not a continuous telemetry freshness and ingestion-control problem. | Provide telemetry-native edge ingestion, open-window freshness, and sketch-state synchronization. |
+| NitroSketch | Sampling sketch updates to reduce sketch CPU. | Focuses on update work for sketches, not multi-sketch observability queries, backend freshness, or GOS/CDM synchronization. | Reuse the sampling idea but compose it with query-level variance budgets and delta suppression. |
+| Continuous Distributed Monitoring / distributed functional monitoring | Communication-efficient tracking of distributed functions. | Mostly a theoretical monitoring model, not an observability pipeline with sketch-family adapters and cold fallback. | Use CDM as the proof model for bounded sketch residual synchronization. |
+| Learned reconstruction systems such as Zoom2Net | Infer fine-grained telemetry from coarse measurements. | Error semantics depend on reconstruction/model behavior rather than preserving query-sufficient sketch state. | Maintain sketch statistics with explicit sketch, sampling, and staleness error terms. |
+
+The intended claim boundary is narrow:
+
+- ASAPCollector does not replace raw telemetry for every task; it provides a
+  warm approximate path plus cold raw fallback.
+- ASAPCollector does not support arbitrary PromQL under one theorem; it supports
+  sketchable/decomposable query classes with family-specific adapters.
+- The fully proved theorem target is fixed-query, fixed-time, additive-state
+  telemetry with independent Horvitz-Thompson update sampling and an
+  update-synchronous or explicitly overshoot-bounded residual protocol.
+- The core claim is that the collector can reduce update work and network
+  traffic while keeping a query-level accuracy envelope.
+
+## 1. Scope and aggregation modes
+
+Continuous monitoring in ASAP is not one protocol. The aggregation axis decides
+which proof model applies.
+
+| Mode | Query shape | Protocol | Coordinator? | Main guarantee |
+| --- | --- | --- | --- | --- |
+| A | per-series $\times$ window | local $\epsilon$-gated delta emission | no | open-window freshness for one local series/group |
+| B | series $\times$ timestamp | mergeable sketch fan-in | no | sealed/instant merge; no monitoring suppression |
+| C | series $\times$ window, fused spatial+temporal | Continuous Distributed Monitoring (CDM) / Geometric-OctoSketch (GOS) over edge collectors | yes | global open-window tracking/alerting across $k$ sites |
+
+This note proves the composition used by modes A and C for the additive-state
+case:
+
+1. sketch approximation,
+2. update sampling,
+3. stale backend state due to suppressed deltas.
+
+For sealed tumbling windows, only the first two sources matter. The merge of
+mergeable summaries does not compound the base sketch error.
+
+## 2. Notation
+
+One fixed tumbling window is the universe of discourse.
+
+| Symbol | Meaning |
+| --- | --- |
+| $k$ | number of stable edge collectors/sites |
+| $i$ | site index, $i \in \{1,\ldots,k\}$ |
+| $u$ | update/sample index |
+| $x_u$ | raw item/value |
+| $f_i$ | local frequency vector at site $i$ |
+| $f=\sum_i f_i$ | global frequency vector |
+| $S_i$ | exact local sketch state for site $i$ |
+| $S=\sum_i S_i$ | exact global additive sketch state |
+| $\widehat S_i$ | sampled local sketch state |
+| $\widehat S=\sum_i \widehat S_i$ | sampled global sketch state |
+| $\widetilde S$ | backend's stale copy of $\widehat S$ |
+| $j$ | sketch cell/bucket index |
+| $a_j$ | linear readout coefficient for query $q(S)=\langle a,S\rangle$ |
+| $p_i$ | sampling probability at site $i$ |
+| $p_{i,r}$ | SDK/source-side sampling probability for row/counter-array $r$ at site/source $i$ |
+| $C_{i,r}$ | candidate counter-update rate or source-to-agent cost weight for site/source $i$, row $r$ |
+| $F_{i,r}$ | row-level protected mass or query sensitivity for site/source $i$, row $r$ |
+| $T_j$ | cell/bucket delta threshold |
+| $V_j$ | cell/bucket activity rate or expected change mass |
+| $B$ | staleness budget for a query/function |
+| $\epsilon_{sk}$ | base sketch error budget |
+| $\epsilon_{sa}$ | sampling error budget |
+| $\epsilon_{cdm}$ | Continuous Distributed Monitoring (CDM) / Geometric-OctoSketch (GOS) staleness budget |
+| $\delta$ | failure probability |
+
+For additive sketches, an update $u$ touches one or more sketch cells. Let
+$j(u)$ be a touched cell in a one-cell-per-update sketch such as a DDSketch
+bucket, or let $j=(r,c)$ be a row/cell pair for CMS/CountSketch.
+
+## 3. Generic error decomposition
+
+For a fixed query $q$ at a fixed time inside a window:
+
+```text
+true answer        q(f)
+exact sketch       q(S)
+sampled sketch     q(\widehat S)
+backend answer     q(\widetilde S)
+```
+
+The triangle inequality gives
+
+```math
+|q(\widetilde S)-q(f)|
+\le
+|q(S)-q(f)|
++ |q(\widehat S)-q(S)|
++ |q(\widetilde S)-q(\widehat S)|.
+```
+
+We name the three terms:
+
+```math
+\mathrm{Err}_{q}
+\le
+\mathrm{Err}^{sk}_{q}
++ \mathrm{Err}^{sa}_{q}
++ \mathrm{Err}^{cdm}_{q}.
+```
+
+For a sealed tumbling window, $\widetilde S=\widehat S$ after boundary emission,
+so
+
+```math
+\mathrm{Err}_{q,sealed}
+\le
+\mathrm{Err}^{sk}_{q}
++ \mathrm{Err}^{sa}_{q}.
+```
+
+For an open window, Continuous Distributed Monitoring (CDM) / Geometric-OctoSketch
+(GOS) controls the third term.
+
+This statement is pointwise: one fixed query and one fixed time. To claim a
+finite workload $Q$ and $M$ possible query times, replace $\delta$ by
+$\delta/(|Q|M)$ in each concentration bound and union bound.
+
+## 4. Additive-state sampling model
+
+### 4.1 Horvitz-Thompson update sampling
+
+In the SDK-side Nitro-style deployment, the sampled unit is not a raw
+observation/packet. A raw measurement is first mapped to the sketch updates it
+would perform, for example one candidate counter update per CountSketch/CMS row.
+The SDK samples those candidate counter-array updates and sends only admitted,
+inverse-probability weighted counter updates to the edge collector. The edge
+collector then merges weighted sketch updates; it does not need the dropped raw
+measurement.
+
+Thus, in this section, $u$ should be read as the sampled update unit. For
+edge-admission baselines, $u$ may be a raw update. For the preferred
+Nitro-style design, $u=(\text{raw item},\text{row/counter-array})$ is one
+candidate sketch counter update.
+
+For sampled update unit $u$, site/source $i(u)$ admits it with probability
+$p_{i(u)}$:
+
+```math
+Z_u \sim \mathrm{Bernoulli}(p_{i(u)}).
+```
+
+Here $Z_u$ is the admission indicator:
+
+```math
+Z_u =
+\begin{cases}
+1, & \text{if update }u\text{ is admitted and applied to the sketch},\\
+0, & \text{if update }u\text{ is skipped}.
+\end{cases}
+```
+
+The inverse-probability weighted contribution of update $u$ is
+
+```math
+Y_u = Z_u/p_{i(u)}.
+```
+
+Equivalently,
+
+```math
+Y_u =
+\begin{cases}
+1/p_{i(u)}, & \text{if update }u\text{ is admitted},\\
+0, & \text{if update }u\text{ is skipped}.
+\end{cases}
+```
+
+Thus $Y_u$ is the effective update weight written into an additive sketch
+counter/cell. Without sampling, the update would contribute weight $1$; with
+sampling, it contributes weight $1/p_{i(u)}$ only on admitted updates.
+
+Then
+
+```math
+\mathbb{E}[Y_u]=1,
+\qquad
+\mathrm{Var}(Y_u)=\frac{1-p_{i(u)}}{p_{i(u)}}.
+```
+
+For a linear readout $q(S)=\langle a,S\rangle$, define $g_u$ as the contribution of update
+$u$ to this readout if it were not sampled. For a one-bucket counter,
+$g_u=a_{j(u)}$. For signed sketches, $g_u$ also includes the row sign.
+
+The sampled readout error is
+
+```math
+X_q
+=q(\widehat S)-q(S)
+=\sum_u (Y_u-1)g_u.
+```
+
+It is unbiased:
+
+```math
+\mathbb{E}[X_q]=0.
+```
+
+The variance follows from the variance of one update contribution. First,
+
+```math
+\mathbb{E}[Y_u^2]
+=
+p_{i(u)}\cdot \frac{1}{p_{i(u)}^2}
+=
+\frac{1}{p_{i(u)}}.
+```
+
+Since $\mathbb{E}[Y_u]=1$,
+
+```math
+\mathrm{Var}(Y_u)
+=
+\mathbb{E}[Y_u^2]-\mathbb{E}[Y_u]^2
+=
+\frac{1}{p_{i(u)}}-1
+=
+\frac{1-p_{i(u)}}{p_{i(u)}}.
+```
+
+Subtracting a constant does not change variance, so
+
+```math
+\mathrm{Var}(Y_u-1)=\mathrm{Var}(Y_u).
+```
+
+Multiplying by the deterministic readout contribution $g_u$ scales variance by
+$g_u^2$:
+
+```math
+\mathrm{Var}((Y_u-1)g_u)
+=
+g_u^2\mathrm{Var}(Y_u)
+=
+g_u^2\frac{1-p_{i(u)}}{p_{i(u)}}.
+```
+
+If the sampling randomness is independent across updates, i.e. the admission
+indicators $Z_u$ are independent,
+
+```math
+\mathrm{Var}(X_q)
+=
+\sum_u g_u^2 \frac{1-p_{i(u)}}{p_{i(u)}}.
+```
+
+This assumption is only about the sampling coin flips, not about the input data
+streams. The metric values or workloads at different sites may be correlated.
+The variance identity needs the random admission decisions to be independent, or
+at least uncorrelated. The high-probability Bernstein bound in Section 4.2 uses
+the stronger independent-sampling assumption.
+
+This equation is the sampling-error budget for query $q$. The random variable
+$X_q=q(\widehat S)-q(S)$ is the difference between answering $q$ from the sampled
+sketch and answering $q$ from the same sketch without update sampling. Each term
+in the sum says how much update $u$ contributes to that sampling error:
+
+- $g_u^2$ is the squared sensitivity of query $q$ to update $u$;
+- $(1-p_{i(u)})/p_{i(u)}$ is the noise introduced by sampling at the site that
+  produced update $u$.
+
+Thus lowering $p_i$ saves update work at site $i$, but increases the variance of
+queries whose relevant updates come from that site. This is the quantity the
+controller constrains when it chooses sampling probabilities.
+
+The independence assumption is what removes covariance terms:
+
+```math
+\mathrm{Var}\left(\sum_u A_u\right)
+=
+\sum_u \mathrm{Var}(A_u)
++2\sum_{u<v}\mathrm{Cov}(A_u,A_v),
+\qquad
+A_u=(Y_u-1)g_u.
+```
+
+If the admission indicators are independent, then the $A_u$ terms are
+independent, so the covariance terms are zero:
+
+```math
+\mathrm{Cov}(A_u,A_v)=0
+\qquad
+(u\ne v).
+```
+
+Therefore,
+
+```math
+\mathrm{Var}\left(\sum_u (Y_u-1)g_u\right)
+=
+\sum_u \mathrm{Var}((Y_u-1)g_u)
+```
+
+when the admission indicators are independent. Intuitively, independent
+sampling decisions mean the per-update sampling errors do not move together, so
+their variances add. If multiple sites reuse the same sampler seed and their
+admissions become correlated, the covariance terms may be nonzero and this
+simplification no longer holds.
+
+Grouped by site:
+
+```math
+\mathrm{Var}(X_q)
+=
+\sum_i \frac{1-p_i}{p_i}
+\sum_{u\in i} g_u^2.
+```
+
+This is only algebraic regrouping: for all updates from site $i$, the sampling
+probability is the same $p_i$, so $(1-p_i)/p_i$ is constant and can be pulled
+outside the inner sum.
+
+For SDK-side Nitro-style sampling, perform the same regrouping by
+site/source-row pair:
+
+```math
+\mathrm{Var}(X_q)
+=
+\sum_{i,r} \frac{1-p_{i,r}}{p_{i,r}}
+\sum_{u\in(i,r)} g_u^2.
+```
+
+Here $u\in(i,r)$ means candidate counter updates generated at source $i$ for row
+or counter array $r$.
+
+This is the quantity the controller must budget. A per-site floor on $p_i$, or a
+per-row floor on $p_{i,r}$, is not sufficient by itself; the variance sum must
+be bounded.
+
+### 4.2 Concentration
+
+Section 4.1 gives a variance:
+
+```math
+\mathrm{Var}(X_q)=\sigma_q^2.
+```
+
+Variance describes the average scale of the sampling fluctuation, but a theorem
+or alert-safety claim needs a high-probability statement:
+
+```text
+with probability at least 1-delta, the sampling error is at most ...
+```
+
+This section converts the variance budget into such a bound. The random
+variable $X_q=q(\widehat S)-q(S)$ is the sampling error of query $q$. The
+quantity $\sigma_q^2$ is the variance derived above. The constant $G$ bounds the
+largest possible contribution of one raw update to the query readout:
+
+```math
+|g_u|\le G.
+```
+
+The value
+
+```math
+p_{\min}=\min_i p_i
+```
+
+is needed because an admitted update from site $i$ is weighted by $1/p_i$; a very
+small sampling probability can make one admitted update large.
+
+For unit updates, bounded $|g_u| \le G$, and independent admission indicators,
+Bernstein's inequality gives, with probability at least $1-\delta$ (derived in
+Appendix A),
+
+```math
+|X_q|
+\le
+\sqrt{2\sigma_q^2\log(2/\delta)}
++ \frac{2G}{3p_{\min}}\log(2/\delta),
+```
+
+where
+
+```math
+\sigma_q^2
+=
+\sum_u g_u^2 \frac{1-p_{i(u)}}{p_{i(u)}},
+\qquad
+p_{\min}=\min_i p_i.
+```
+
+The first term,
+
+```math
+\sqrt{2\sigma_q^2\log(2/\delta)},
+```
+
+is the variance-driven part of the bound. The second term,
+
+```math
+\frac{2G}{3p_{\min}}\log(2/\delta),
+```
+
+is the worst-case single-update correction: inverse-probability weighting can
+amplify an admitted update by as much as $1/p_{\min}$.
+
+For a uniform sampling probability $p$ and a point-frequency count $f(x)=N(x)$,
+the usual relative sampling term is
+
+```math
+\epsilon_{sa}(x,\delta)
+=
+O\left(
+\sqrt{\frac{(1-p)\log(1/\delta)}{p\,N(x)}}
++\frac{\log(1/\delta)}{p\,N(x)}
+\right).
+```
+
+The one-standard-deviation shorthand is
+
+```math
+\epsilon_{sa}(x)
+=
+\sqrt{\frac{1-p}{p\,N(x)}}.
+```
+
+Use the high-probability form in theorem statements and alert safety claims.
+
+## 5. SDK-side Nitro-style counter-update sampling
+
+NitroSketch does not advocate naive packet-level sampling as the main design.
+Its key observation is that packet-level sampling can give poor sketch accuracy:
+when a packet is dropped before the sketch, all rows/counter arrays miss the same
+packet, so the sampling noise is correlated across rows and median/min
+amplification cannot remove that shared error. NitroSketch instead samples
+sketch counter-array updates and applies inverse-probability weights to the
+arrays that are updated. This preserves the expected counter value while keeping
+row-level sampling noise closer to the sketch's native amplification model.
+
+The ASAPCollector design should follow that distinction at the telemetry source.
+The OTel SDK/data source should not sample whole measurements before sketching.
+Instead, it should:
+
+1. map a raw measurement $x$ to the sketch counter updates it would have
+   produced, e.g. $(r,h_r(x),s_r(x))$ for CountSketch;
+2. independently sample each candidate row/counter-array update with probability
+   $p_{i,r}$;
+3. send only admitted weighted counter updates to the edge collector, with
+   update weight $1/p_{i,r}$ times the original signed/count contribution;
+4. let the edge collector merge these weighted sketch updates into its local
+   sketch state.
+
+This reduces traffic from OTel SDK/data source to the agent collector without
+falling back to packet-level sampling. The wire unit is a sampled sketch counter
+update, not a raw measurement. For multi-row sketches, this preserves
+independent row-level sampling noise, so CountSketch median and CMS row
+amplification remain meaningful.
+
+For a known point query/key $x$, the controller may choose probabilities
+$p_{i,r}$ across telemetry sources/sites $i$ and sketch rows/counter arrays $r$.
+Let $F_{i,r}(x)$ be the row-level protected mass/sensitivity for that query, and
+let $C_{i,r}$ be the candidate counter-update rate or source-to-agent wire-cost
+weight for source $i$, row $r$. Under SDK-side counter-update sampling, the
+expected source-to-agent traffic and edge update work are proportional to
+$C_{i,r}p_{i,r}$.
+
+The query-specific cost minimization problem is
+
+```math
+\begin{aligned}
+\min_{0<p_{i,r}\le 1}\quad & \sum_{i,r} C_{i,r}p_{i,r} \\
+\text{s.t.}\quad & \sum_{i,r} F_{i,r}(x)\frac{1-p_{i,r}}{p_{i,r}} \le V .
+\end{aligned}
+```
+
+Ignoring clamps, the Lagrangian is
+
+```math
+\mathcal{L}
+=
+\sum_{i,r} C_{i,r}p_{i,r}
++\lambda\left(\sum_{i,r}F_{i,r}(x)\frac{1-p_{i,r}}{p_{i,r}}-V\right).
+```
+
+Since $(1-p_{i,r})/p_{i,r} = 1/p_{i,r} - 1$,
+
+```math
+\frac{\partial \mathcal{L}}{\partial p_{i,r}}
+=
+C_{i,r}-\lambda F_{i,r}(x)/p_{i,r}^2.
+```
+
+The interior optimum satisfies
+
+```math
+p_{i,r}
+=
+\sqrt{\lambda}\sqrt{\frac{F_{i,r}(x)}{C_{i,r}}}.
+```
+
+The scalar $\sqrt{\lambda}$ is selected so that the variance constraint binds,
+and the result is clamped to $(0,1]$.
+
+This is the Nitro-style control-plane extension. It is not stated as a theorem
+in the NitroSketch paper, but it uses the same counter-array sampling unit.
+
+For comparison, an edge-admission baseline would choose one probability $p_i$
+per source and admit/drop the entire raw update. That baseline has objective
+$\sum_i R_i p_i$ and variance term $\sum_i F_i(1-p_i)/p_i$, yielding
+$p_i\propto\sqrt{F_i/R_i}$ under the same algebra. That baseline is appropriate
+for scalar sums/counts and additive bucket counts, but for CountSketch/CMS it can
+inherit the packet-level sampling weakness NitroSketch warns about and should
+not be used as the main design.
+
+For whole-sketch or unknown-key deployments, replace the query-specific
+$F_{i,r}(x)$ by the protected row/counter-array mass for the query class. A
+conservative whole-sketch floor commonly used in deployments is derived from
+
+```math
+\frac{1-p_{i,r}}{p_{i,r}M_{i,r}}\le \epsilon_{sa}^2,
+```
+
+where $M_{i,r}$ is an observable per-source, per-row mass. This gives
+
+```math
+p_{i,r} \ge \frac{1}{1+\epsilon_{sa}^2 M_{i,r}}.
+```
+
+This floor protects aggregate sketch quality without pretending to optimize for
+one known key. In implementation, the controller can collapse $p_{i,r}$ to a
+shared $p_i$ only for sketch families where whole-update admission does not
+break the intended estimator.
+
+## 6. Continuous Distributed Monitoring (CDM) / Geometric-OctoSketch (GOS) staleness model
+
+### 6.1 Residual invariant
+
+Let $A_i[j]$ be the last state for site $i$, cell $j$, that the backend has
+acknowledged. The unshipped residual is
+
+```math
+\rho_i[j](t)=\widehat S_i[j](t)-A_i[j](t).
+```
+
+If the edge protocol guarantees
+
+```math
+|\rho_i[j](t)|\le T_j
+```
+
+for every site and cell, then
+
+```math
+|\widehat S[j](t)-\widetilde S[j](t)|
+=
+\left|\sum_i \rho_i[j](t)\right|
+\le
+\sum_i |\rho_i[j](t)|
+\le
+kT_j.
+```
+
+For a linear readout $q(S)=\langle a,S\rangle$:
+
+```math
+\mathrm{Err}^{cdm}_q
+=
+|q(\widetilde S)-q(\widehat S)|
+\le
+k\sum_j |a_j|T_j.
+```
+
+If the implementation checks thresholds only every $\Delta_{check}$ seconds, not
+after every update, include an overshoot term. If site $i$, cell $j$ can change
+at rate at most $U_{ij}$ between checks, then
+
+```math
+|\rho_i[j](t)|\le T_j+U_{ij}\Delta_{check},
+```
+
+and
+
+```math
+\mathrm{Err}^{cdm}_q
+\le
+\sum_j |a_j|
+\sum_i (T_j+U_{ij}\Delta_{check}).
+```
+
+Use the no-overshoot form only for an update-synchronous threshold check.
+
+### 6.2 Sampling / Continuous Distributed Monitoring (CDM) coupling
+
+Sampling perturbs the value tested by the CDM threshold. If the sampling noise
+is larger than the CDM band, the value can jitter across the threshold and cause
+spurious emits or delayed alerts. A practical coupling rule is
+
+```math
+\epsilon_{sa}(\delta) \le \epsilon_{cdm}.
+```
+
+Equivalently, for per-cell thresholds, avoid transmitting finer than the
+sampling noise scale. If $N_j(\Delta)$ is the expected true cell mass over the
+control horizon $\Delta$, a heuristic floor is
+
+```math
+T_j^{floor}
+\asymp
+\sqrt{N_j(\Delta)(1-p_j)/p_j}.
+```
+
+This is not a theorem-level threshold by itself. The exact constant and the
+choice of $N_j(\Delta)$ depend on the concentration bound, the failure
+probability allocation, and whether the controller protects one query, a query
+class, or a whole sketch.
+
+## 7. Geometric-OctoSketch (GOS) threshold water-filling
+
+For additive sketch cells, use the threshold-crossing cost model in which the
+communication/upload rate of cell $j$ is proportional to $V_j/T_j$: higher
+activity or smaller thresholds cause more emits. This is a modeling assumption,
+not a protocol invariant; bursty cells, signed oscillations, batching, ACK delay,
+and retries can change the constant or require an overshoot term.
+
+Given this model and a staleness budget $B$, solve
+
+```math
+\begin{aligned}
+\min_{T_j>0}\quad & \sum_j \frac{V_j}{T_j} \\
+\text{s.t.}\quad & \sum_j c_jT_j \le B ,
+\end{aligned}
+```
+
+where
+
+```math
+c_j = k|a_j|
+```
+
+for a linear query. For a differentiable monitored functional, use
+
+```math
+c_j = k|g_j|,
+\qquad
+g_j=\partial F/\partial S[j],
+```
+
+plus a separate curvature budget if the functional is nonlinear.
+
+The Lagrangian is
+
+```math
+\mathcal{L}
+=
+\sum_j \frac{V_j}{T_j}
++\lambda\left(\sum_j c_jT_j-B\right).
+```
+
+The first-order condition is
+
+```math
+\frac{\partial \mathcal{L}}{\partial T_j}
+=
+-\frac{V_j}{T_j^2}+\lambda c_j=0.
+```
+
+Thus
+
+```math
+T_j=\sqrt{\frac{V_j}{\lambda c_j}}.
+```
+
+Solving for $\lambda$ from the active budget constraint:
+
+```math
+\sum_j c_j\sqrt{\frac{V_j}{\lambda c_j}}
+=B,
+```
+
+so
+
+```math
+\frac{1}{\sqrt{\lambda}}
+=
+\frac{B}{\sum_\ell \sqrt{c_\ell V_\ell}}.
+```
+
+The closed form is
+
+```math
+\boxed{
+T_j
+=
+\frac{B\sqrt{V_j/c_j}}
+{\sum_\ell \sqrt{c_\ell V_\ell}}
+}.
+```
+
+With floors and caps:
+
+```math
+T_j
+=
+\mathrm{clamp}\left(
+\frac{B\sqrt{V_j/c_j}}
+{\sum_\ell \sqrt{c_\ell V_\ell}},
+\;
+T_j^{floor},
+\;
+\min(T_j^{query}, V_j\Delta^*)
+\right).
+```
+
+Clamped cells consume or release budget; recompute the water-filling expression
+over the remaining free cells until no cell changes clamp status.
+
+## 8. Sketch-family instantiations
+
+### 8.1 Sum / Count
+
+State:
+
+```math
+S_i=\sum_{u\in i} x_u
+```
+
+or $S_i=N_i$ for count.
+
+Base sketch error:
+
+```math
+\mathrm{Err}^{sk}=0
+```
+
+up to floating-point arithmetic.
+
+Sampling error for unit counts under uniform $p$:
+
+```math
+|\widehat N-N|
+\le
+O\left(
+\sqrt{\frac{N\log(1/\delta)}{p}}
++\frac{\log(1/\delta)}{p}
+\right).
+```
+
+CDM staleness with scalar threshold $T$:
+
+```math
+\mathrm{Err}^{cdm}\le kT.
+```
+
+For a threshold alert $N>\tau$, deterministic no-missed-crossing holds only in
+the unsampled monotone setting. With sampling, use a high-probability margin.
+
+### 8.2 Count-Min Sketch point query
+
+State: $d \times w$ nonnegative counter matrix.
+
+Point query for key $x$:
+
+```math
+\widehat f_{CMS}(x)
+=
+\min_r S[r,h_r(x)].
+```
+
+Base sketch error:
+
+```math
+f(x)
+\le
+\widehat f_{CMS}(x)
+\le
+f(x)+\epsilon_{sk}N
+```
+
+with probability at least $1-\delta_{sk}$, for the standard choice of width/depth.
+
+Sampling error applies to every row counter. For row $r$, define
+
+```math
+X_r(x)
+=
+\widehat S[r,h_r(x)]-S[r,h_r(x)].
+```
+
+Then $X_r(x)$ is unbiased with variance controlled by the updates landing in
+that cell:
+
+```math
+\mathrm{Var}(X_r(x))
+=
+\sum_{u:h_r(x_u)=h_r(x)}
+\frac{1-p_{i(u)}}{p_{i(u)}}.
+```
+
+A conservative high-probability point statement can be made by union-bounding
+the row-level sampling events with the usual CMS collision event:
+
+```math
+\widetilde f_{CMS}(x)
+\le
+f(x)
++\epsilon_{sk}N
++m_{sa}(x,\delta)
++k\max_r T_{r,h_r(x)}.
+```
+
+Because CMS uses a $\min$, the usual no-underestimate property is not preserved
+by two-sided sampling noise. This makes CMS a caveated adapter rather than the
+clean first theorem target. For alerting, fire with a confidence margin:
+
+```math
+\widetilde f_{CMS}(x)+m_{sa}(x,\delta)+m_{cdm}(x)
+\ge
+(1-\epsilon)\tau.
+```
+
+CMS is therefore useful for upper-bound-oriented point queries, but sampled CMS
+alerts should be presented as probabilistic and margin-based. CountSketch, Sum,
+and DDSketch range counts are cleaner first theorem targets for sampled GOS.
+
+### 8.3 CountSketch point query
+
+State: $d \times w$ signed counter matrix.
+
+Row estimate:
+
+```math
+E_r(x)=s_r(x)S[r,h_r(x)].
+```
+
+Point estimate:
+
+```math
+\widehat f_{CS}(x)=\mathrm{median}_{r=1}^d E_r(x).
+```
+
+Base sketch error:
+
+```math
+|\widehat f_{CS}(x)-f(x)|
+\le
+\epsilon_{sk}\|f\|_2
+```
+
+with probability at least $1-\delta_{sk}$.
+
+Sampling error is two-sided and unbiased at each row. Let
+
+```math
+X_r(x)
+=
+s_r(x)(\widehat S[r,h_r(x)]-S[r,h_r(x)]).
+```
+
+Then
+
+```math
+\mathbb{E}[X_r(x)]=0,
+```
+
+and its variance is the sum of inverse-probability variances of the sampled
+updates colliding into the queried cell. A median-of-rows bound follows by
+combining row-level concentration with the usual CountSketch row amplification.
+This row amplification statement assumes Nitro-style row/counter-array sampling
+or otherwise independent row-level sampling noise. If the implementation uses
+edge admission sampling, the true-key sampling noise is shared across rows; then
+the sampling term must be bounded before the median step and should not be
+credited with CountSketch row amplification.
+
+CDM staleness for row $r$ is
+
+```math
+|s_r(x)(\widetilde S-\widehat S)[r,h_r(x)]|
+\le
+kT_{r,h_r(x)}.
+```
+
+Since the median is 1-Lipschitz in the $L_\infty$ perturbation across rows:
+
+```math
+|\mathrm{median}_r(a_r+e_r)-\mathrm{median}_r(a_r)|
+\le
+\max_r |e_r|,
+```
+
+the point-query staleness satisfies
+
+```math
+\mathrm{Err}^{cdm}_{CS}(x)
+\le
+k\max_r T_{r,h_r(x)}.
+```
+
+Thus a paper-safe fixed-time statement is
+
+```math
+|\widetilde f_{CS}(x)-f(x)|
+\le
+\epsilon_{sk}\|f\|_2
++m_{sa}(x,\delta)
++k\max_r T_{r,h_r(x)}.
+```
+
+CountSketch is the cleanest first target for sampled GOS: additive state,
+two-sided estimator, and no CMS one-sided caveat.
+
+### 8.4 DDSketch value-range counts
+
+DDSketch maps a positive value $x$ to a logarithmic bucket $b(x)$. The bucket
+mapping is chosen so values in one bucket have relative value error at most
+$\alpha$.
+
+For a value range $[L,U]$, define a linear bucket readout
+
+```math
+q_{[L,U]}(S)
+=
+\sum_{b: v_b\in[L,U]} S[b],
+```
+
+where $v_b$ is the representative value of bucket $b$.
+
+This is an additive nonnegative aggregate. Sampling and CDM compose exactly as
+in the generic additive-state theorem:
+
+```math
+|q_{[L,U]}(\widetilde S)-q_{[L,U]}(S)|
+\le
+m_{sa}([L,U],\delta)
++k\sum_{b:v_b\in[L,U]}T_b.
+```
+
+This is the DDSketch query class that behaves like a scalar/bucket-count monitor.
+
+### 8.5 DDSketch quantiles
+
+DDSketch quantile queries are not linear readouts. They depend on prefix counts.
+Sampling and CDM should therefore be expressed as rank error.
+
+Let the true bucket count be
+
+```math
+n_b=\sum_{u=1}^N 1\{b(x_u)=b\}.
+```
+
+Let
+
+```math
+N_{\le b}=\sum_{j\le b}n_j.
+```
+
+#### Implemented thinning view
+
+The current DDSketch implementation performs value-independent uniform admission
+before the bucket update: if the item is not admitted, the entire update is
+skipped. This subsection assumes a single uniform probability $p$ for all items.
+Let
+
+```math
+Z_u\sim\mathrm{Bernoulli}(p).
+```
+
+The sampled bucket count is
+
+```math
+n_b^{sample}
+=
+\sum_{u:b(x_u)=b}Z_u.
+```
+
+Quantile lookup over the sampled DDSketch uses the sampled total
+
+```math
+M=\sum_u Z_u.
+```
+
+Uniform thinning preserves the distribution in expectation. With high
+probability, the empirical CDF of the sampled stream is close to the true CDF.
+For a finite set of $B$ nonempty buckets, a union bound over bucket prefixes
+gives
+
+```math
+\sup_b
+\left|
+\frac{N^{sample}_{\le b}}{M}
+-
+\frac{N_{\le b}}{N}
+\right|
+\le
+\epsilon_{sa}
+```
+
+with
+
+```math
+\epsilon_{sa}
+=
+O\left(
+\sqrt{\frac{\log(B/\delta)}{pN}}
++\frac{\log(B/\delta)}{pN}
+\right),
+```
+
+assuming $pN$ is not too small.
+
+#### Horvitz-Thompson analysis view
+
+Equivalently, for analysis one may assign every admitted item weight $1/p$:
+
+```math
+Y_u=Z_u/p.
+```
+
+Then
+
+```math
+\widehat n_b=\sum_{u:b(x_u)=b}Y_u
+```
+
+is an unbiased estimator of $n_b$, and
+
+```math
+\mathrm{Var}(\widehat N_{\le b})
+=
+N_{\le b}\frac{1-p}{p}.
+```
+
+Bernstein plus a union bound over $B$ prefixes yields
+
+```math
+\sup_b
+|\widehat N_{\le b}-N_{\le b}|
+\le
+O\left(
+\sqrt{\frac{N\log(B/\delta)}{p}}
++\frac{\log(B/\delta)}{p}
+\right).
+```
+
+Dividing by $N$ gives the same rank scale:
+
+```math
+\epsilon_{sa}
+=
+O\left(
+\sqrt{\frac{\log(B/\delta)}{pN}}
++\frac{\log(B/\delta)}{pN}
+\right).
+```
+
+This weighted view is useful for proof. The implemented unweighted sampled
+quantile returns the same bucket as the uniformly weighted view, because every
+admitted item has the same weight. Counts, however, need `sample_p` rescaling if
+they are queried as counts.
+
+If the controller uses nonuniform per-site probabilities $p_i$, the unweighted
+sampled DDSketch quantile is biased toward high-$p_i$ sites. A nonuniform
+deployment needs weighted quantile semantics, a resampling correction, or a
+separate proof for the chosen estimator.
+
+#### CDM rank staleness
+
+If bucket residuals satisfy $|\rho_i[b]|\le T_b$, then for any prefix:
+
+```math
+|\widetilde N_{\le b}-\widehat N_{\le b}|
+\le
+k\sum_{j\le b}T_j.
+```
+
+Hence the CDM-induced rank error is
+
+```math
+\epsilon_{cdm}
+=
+\frac{k}{N}
+\sup_b\sum_{j\le b}T_j.
+```
+
+If the backend's total count is also stale, allocate a small additional budget
+for denominator error, or normalize by a lower bound on $N$.
+
+#### DDSketch quantile corollary
+
+Let $\widetilde x_q$ be the sampled and stale DDSketch estimate of the
+$q$-quantile. With probability at least $1-\delta$,
+
+```math
+\boxed{
+(1-\alpha)x_{q-\epsilon_{sa}-\epsilon_{cdm}}
+\le
+\widetilde x_q
+\le
+(1+\alpha)x_{q+\epsilon_{sa}+\epsilon_{cdm}}
+}.
+```
+
+Thus DDSketch keeps its multiplicative value error $\alpha$, while sampling and
+CDM/GOS add rank error.
+
+### 8.6 KLL quantiles
+
+KLL is mergeable, but it is not a subtractive additive counter array. It should
+not be forced into the per-cell linear residual theorem.
+
+For a sealed window, mergeability preserves the KLL rank guarantee:
+
+```math
+|\mathrm{rank}(\widehat x_q)-qN|
+\le
+\epsilon_{KLL}N
+```
+
+with the configured KLL failure probability.
+
+If updates are uniformly sampled before KLL insertion, the same thinning rank
+term appears:
+
+```math
+\epsilon_{sa}
+=
+O\left(
+\sqrt{\frac{\log(1/\delta)}{pN}}
++\frac{\log(1/\delta)}{pN}
+\right).
+```
+
+For open-window freshness, use a segment model: unshipped KLL segments contain
+$R$ samples. Then the stale-rank contribution is bounded by
+
+```math
+\epsilon_{cdm}
+\le
+R/N.
+```
+
+The combined quantile rank error is
+
+```math
+\epsilon_{rank}
+\le
+\epsilon_{KLL}
++\epsilon_{sa}
++R/N.
+```
+
+KLL is therefore a mergeable-summary adapter, not a GOS per-cell water-filling
+instance.
+
+### 8.7 HyperLogLog distinct count
+
+HLL state is a vector of max registers, with merge defined by register-wise max.
+It is mergeable but non-additive.
+
+Base error:
+
+```math
+\epsilon_{HLL}\approx 1.04/\sqrt{m}
+```
+
+for $m$ registers.
+
+Nitro-style inverse-probability update sampling is not appropriate for HLL
+register updates: the update is a max operation, not an additive counter
+increment. Hash-threshold distinct sampling can be analyzed separately, but it
+does not fit the additive sampling theorem above.
+
+For CDM/freshness, use a family-specific register-change adapter. The linear
+per-cell bound $k\sum_j |a_j|T_j$ does not apply directly.
+
+## 9. Nonlinear monitored functionals
+
+For differentiable functionals $F(S)$, write the stale perturbation as
+
+```math
+e=\widetilde S-\widehat S.
+```
+
+At reference state $S_0$,
+
+```math
+F(S_0+e)-F(S_0)
+=
+\langle \nabla F(S_0),e\rangle
++R_2(e).
+```
+
+If the Hessian spectral norm is bounded by $\lambda$, then
+
+```math
+|R_2(e)|\le \frac{1}{2}\lambda\|e\|_2^2.
+```
+
+The first-order term can be controlled by GOS water-filling with
+
+```math
+c_j=k|\nabla_jF(S_0)|.
+```
+
+The curvature term needs a separate budget. Do not spend the whole error budget
+on the first-order term unless $\lambda=0$.
+
+### F2 example
+
+For
+
+```math
+F(S)=\|S\|_2^2,
+```
+
+we have
+
+```math
+F(S+e)-F(S)
+=
+2\langle S,e\rangle+\|e\|_2^2.
+```
+
+Therefore
+
+```math
+|F(S+e)-F(S)|
+\le
+2\|S\|_2\|e\|_2+\|e\|_2^2.
+```
+
+To guarantee relative error $\epsilon$:
+
+```math
+2\|S\|_2\|e\|_2+\|e\|_2^2
+\le
+\epsilon\|S\|_2^2.
+```
+
+Let $\alpha_e=\|e\|_2/\|S\|_2$. Then
+
+```math
+2\alpha_e+\alpha_e^2\le \epsilon,
+```
+
+so it suffices to enforce
+
+```math
+\alpha_e
+\le
+\sqrt{1+\epsilon}-1.
+```
+
+If thresholds are isotropic, $T_j=T$ for $n$ sketch cells and each site residual
+is bounded by $T$, then
+
+```math
+\|e\|_2
+\le
+k\sqrt{n}T.
+```
+
+Thus a curvature-safe isotropic threshold is
+
+```math
+T
+\le
+\frac{(\sqrt{1+\epsilon}-1)\|S\|_2}
+{k\sqrt{n}}.
+```
+
+For small $\epsilon$, this is approximately
+
+```math
+T\lesssim \frac{\epsilon\|S\|_2}{2k\sqrt{n}},
+```
+
+but the exact expression should be used in formal claims.
+
+## 10. Alert mode
+
+Threshold/alert monitoring is different from value-estimation. In the
+unsampled monotone case, slack-countdown can provide deterministic no-missed
+crossing. With two-sided sampling noise, alert safety is probabilistic.
+
+For a global monitored value $G$, sampled estimate $\widehat G$, and sampling
+margin $m_{sa}(\delta)$, a safe fire rule is
+
+```math
+\widehat G + m_{sa}(\delta) + m_{cdm}
+\ge
+(1-\epsilon)\tau.
+```
+
+Here $m_{cdm}$ is the worst-case stale backend margin. For a linear additive
+readout:
+
+```math
+m_{cdm}=k\sum_j |a_j|T_j.
+```
+
+**Implementation (scalar coordinator).** `Monitor::rebroadcast`
+(`data_plane/src/monitor/coordinator.rs`) fires when
+$\widehat G + m_{sa} \ge (1-\epsilon)\tau$, i.e. it folds $m_{sa}$ into the band.
+The $m_{cdm}$ term is not added separately because the slack countdown already
+realizes it: $\widehat G=\sum_i \mathrm{known\_value}_i$ is a lower bound each
+edge maintains within its granted slack, so firing on $\widehat G$ is already
+$m_{cdm}$-safe. The sampling term is the aggregate 1-σ standard deviation
+$m_{sa}=\sqrt{\sum_i \mathrm{kv}_i(1-p_i)/p_i}$, with $p_i$ the coordinator's own
+allocated floor $1/(1+\epsilon^2\,\mathrm{rate}_i)$ (self-consistent with the
+`sample_p` it grants). It is **0 whenever sampling is not granted** (a single
+edge, or `rate=0`), so the unsampled path is unchanged. Caveats: this is the 1-σ
+heuristic (not the high-probability Bernstein margin — a theorem-grade alert
+multiplies by $z_\delta$); and the $\mathrm{kv}_i(1-p_i)/p_i$ variance assumes a
+unit-weight count readout (exact for CMS/CountSketch point counts, conservative
+for a non-unit-weight Sum — where the margin only fires earlier, never later).
+
+For quantiles, convert the alert predicate to a rank/count predicate first. For
+example, a DDSketch predicate $q_\phi > v^*$ is equivalent to a bucket-prefix
+count predicate up to the DDSketch value bucket error. The sampling and CDM
+terms then enter as rank/count margins.
+
+## 11. Summary table
+
+| Family/query | Sampling term | Continuous Distributed Monitoring (CDM) / Geometric-OctoSketch (GOS) term | Safe paper claim |
+| --- | --- | --- | --- |
+| Sum/count | scalar Bernoulli count variance | $kT$ | exact base sketch plus sampling plus scalar staleness |
+| CMS point | row-counter sampling variance | $k\max_r T_{r,h_r(x)}$ | caveated adapter: row union plus collision event; probabilistic alert margin |
+| CountSketch point | row-counter sampling variance | $k\max_r T_{r,h_r(x)}$ | clean additive/two-sided composition; best first theorem target |
+| DDSketch range count | bucket-count sampling variance | $k\sum_{b\in \mathrm{range}}T_b$ | additive bucket-count composition |
+| DDSketch quantile | rank error $\epsilon_{sa}$ | prefix rank error $\epsilon_{cdm}$ | $(1\pm \alpha)$ value error around $q\pm \epsilon$ rank |
+| KLL quantile | thinning rank error | unshipped segment rank $R/N$ | mergeable adapter, not per-cell GOS |
+| HLL distinct | exclude Nitro-style additive sampling | register-change adapter | mergeable but non-additive; family-specific proof |
+
+## 12. Paper theorem templates
+
+### Theorem 1: additive-state composition
+
+For a fixed linear query $q(S)=\langle a,S\rangle$ at a fixed time in a tumbling window,
+assume:
+
+1. sampling randomness is independent across updates, i.e. the admission
+   indicators $Z_u$ are independent;
+2. sampled updates use inverse-probability weights for additive counters;
+3. each site/cell residual is bounded by $R_{ij}$; in the update-synchronous
+   ideal protocol $R_{ij}=T_j$, while periodic checks can use
+   $R_{ij}=T_j+U_{ij}\Delta_{check}$;
+4. the base sketch error is at most $E_{sk}(q)$ with probability
+   $1-\delta_{sk}$.
+
+Then with probability at least $1-\delta_{sk}-\delta_{sa}$,
+
+```math
+|q(\widetilde S)-q(f)|
+\le
+E_{sk}(q)
++m_{sa}(q,\delta_{sa})
++\sum_j |a_j|\sum_i R_{ij}.
+```
+
+If only a variance statement is needed, pairwise uncorrelated admission
+indicators are sufficient for the variance identity, but not for the Bernstein
+margin above. If all sites use the update-synchronous bound $R_{ij}=T_j$, the
+staleness term reduces to $k\sum_j |a_j|T_j$.
+
+### Theorem 2: Geometric-OctoSketch (GOS) threshold allocation
+
+Under the threshold-crossing cost model, given additive-state staleness budget
+$B$ and cell activity $V_j$, the threshold vector minimizing $\sum_j V_j/T_j$
+subject to $\sum_j c_jT_j\le B$ is
+
+```math
+T_j
+=
+\frac{B\sqrt{V_j/c_j}}
+{\sum_\ell\sqrt{V_\ell c_\ell}},
+```
+
+before floors and caps. The clamped solution is obtained by iterative
+water-filling over unclamped cells.
+
+### Corollary: DDSketch sampled open-window quantile
+
+For DDSketch relative value parameter $\alpha$, uniform value-independent
+admission probability $p$, $B$ nonempty buckets, and bucket residual thresholds
+$T_b$, with probability at least $1-\delta$:
+
+```math
+(1-\alpha)x_{q-\epsilon_{sa}-\epsilon_{cdm}}
+\le
+\widetilde x_q
+\le
+(1+\alpha)x_{q+\epsilon_{sa}+\epsilon_{cdm}},
+```
+
+where
+
+```math
+\epsilon_{sa}
+=
+O\left(
+\sqrt{\frac{\log(B/\delta)}{pN}}
++\frac{\log(B/\delta)}{pN}
+\right),
+```
+
+and
+
+```math
+\epsilon_{cdm}
+=
+\frac{k}{N}\sup_b\sum_{j\le b}T_j.
+```
+
+## 13. Implementation notes
+
+- The Go DDSketch wrapper performs value-independent admission before the
+  bucket update. This is equivalent to uniform thinning for quantile rank
+  analysis. Counts need `sample_p` rescaling if they are queried as counts.
+- CountSketch sparse deltas currently require integral cell deltas. If sampling
+  creates fractional weighted cells, the implementation can fall back to full
+  frames. That preserves accuracy but weakens communication claims for the
+  sampled+delta combination unless a fractional delta wire is added.
+- SDK-side Nitro-style sampling requires a wire representation for sampled
+  weighted counter updates from SDK/data source to agent collector. For signed
+  sketches, the payload must preserve row, column, sign, and weight
+  $1/p_{i,r}$.
+- The strongest continuous $|\rho_i[j]|\le T_j$ proof assumes an update-synchronous
+  threshold check. Current sub-window/tick-based emit paths should be stated
+  with an overshoot term in formal claims.
+- For SDK-source or multi-unit sampling, the sampling randomness must make the
+  admission indicators independent per `(edge_id, agg_id, window)` or hash-based
+  per item. Shared sampler seeds can correlate admissions and invalidate
+  variance-addition by introducing covariance terms.
+
+## 14. Evaluation checklist
+
+The system claim is only credible if the evaluation shows that ASAPCollector
+moves work off the central raw-sample path while preserving the advertised error
+envelope for supported queries. At minimum, measure:
+
+- **SDK/source update work:** candidate sketch counter updates/sec, admitted
+  counter updates/sec, CPU, and memory as $p_{i,r}$ changes.
+- **Source-to-agent network load:** bytes/sec and messages/sec for raw OTLP
+  export versus sampled weighted counter-update export.
+- **Agent-to-backend network load:** bytes/sec and messages/sec for periodic
+  sketch export and GOS thresholded deltas.
+- **Backend ingest load:** accepted samples/sec or summary frames/sec, WAL/queue
+  pressure, index/storage growth, and write amplification if applicable.
+- **Freshness:** open-window staleness measured as both wall-clock lag and
+  query-space error $\mathrm{Err}_q^{cdm}$.
+- **Accuracy envelope:** empirical query error decomposed into sketch error,
+  sampling error, and staleness error; report coverage of the claimed
+  high-probability bound.
+- **Controller behavior:** selected $p_{i,r}$ and $T_j$ under hot/cold sources,
+  query-sensitive/query-insensitive rows or cells, and changing workloads.
+- **Fallback boundary:** unsupported query classes and cold raw fallback cost,
+  so the paper does not imply arbitrary PromQL support.
+- **Failure modes:** tick-based overshoot, delayed ACKs, retries, collector
+  restart, and correlated sampler seeds.
+
+Use CountSketch, Sum/count, and DDSketch range counts as the first theorem-backed
+accuracy experiments. Treat CMS, DDSketch quantiles, KLL, and HLL as
+family-specific adapter experiments with their own stated caveats.
+
+## Appendix A. Bernstein inequality step
+
+This appendix derives the concentration bound used in Section 4.2:
+
+```math
+|X_q|
+\le
+\sqrt{2\sigma_q^2\log(2/\delta)}
++\frac{2G}{3p_{\min}}\log(2/\delta)
+```
+
+with probability at least $1-\delta$.
+
+Recall the sampled query error:
+
+```math
+X_q
+=
+\sum_u (Y_u-1)g_u.
+```
+
+Define the centered per-update random variable
+
+```math
+A_u=(Y_u-1)g_u.
+```
+
+Then
+
+```math
+X_q=\sum_u A_u.
+```
+
+### A.1 Zero mean
+
+Because $E[Y_u]=1$,
+
+```math
+\mathbb{E}[A_u]
+=
+\mathbb{E}[(Y_u-1)g_u]
+=
+g_u(\mathbb{E}[Y_u]-1)
+=0.
+```
+
+Thus $X_q$ is a sum of independent centered random variables, assuming the
+sampling admission indicators are independent.
+
+### A.2 Variance parameter
+
+From Section 4.1:
+
+```math
+\mathrm{Var}(A_u)
+=
+g_u^2\frac{1-p_{i(u)}}{p_{i(u)}}.
+```
+
+Therefore the total variance parameter is
+
+```math
+\sigma_q^2
+=
+\sum_u \mathrm{Var}(A_u)
+=
+\sum_u g_u^2\frac{1-p_{i(u)}}{p_{i(u)}}.
+```
+
+### A.3 Uniform bound on one summand
+
+Bernstein's inequality also needs an almost-sure bound on $|A_u|$.
+
+Since
+
+```math
+Y_u =
+\begin{cases}
+1/p_{i(u)}, & \text{if update }u\text{ is admitted},\\
+0, & \text{if update }u\text{ is skipped},
+\end{cases}
+```
+
+we have
+
+```math
+Y_u-1 =
+\begin{cases}
+(1-p_{i(u)})/p_{i(u)}, & \text{if update }u\text{ is admitted},\\
+-1, & \text{if update }u\text{ is skipped}.
+\end{cases}
+```
+
+Thus
+
+```math
+|Y_u-1|
+\le
+\frac{1}{p_{i(u)}}
+\le
+\frac{1}{p_{\min}}.
+```
+
+If $|g_u| \le G$, then
+
+```math
+|A_u|
+=
+|(Y_u-1)g_u|
+\le
+\frac{G}{p_{\min}}.
+```
+
+Let
+
+```math
+M=\frac{G}{p_{\min}}.
+```
+
+### A.4 Apply Bernstein
+
+One standard two-sided Bernstein bound for independent centered random variables
+$A_u$ with $|A_u| \le M$ and variance sum $\sigma_q^2$ is
+
+```math
+\Pr\left[
+\left|\sum_u A_u\right|
+\ge
+\sqrt{2\sigma_q^2 t}
++\frac{2M}{3}t
+\right]
+\le
+2e^{-t}.
+```
+
+Set
+
+```math
+t=\log(2/\delta).
+```
+
+Then $2e^{-t}=\delta$, so with probability at least $1-\delta$,
+
+```math
+\left|\sum_u A_u\right|
+\le
+\sqrt{2\sigma_q^2\log(2/\delta)}
++\frac{2M}{3}\log(2/\delta).
+```
+
+Substituting $M=G/p_{\min}$ and $X_q=sum_u A_u$ gives
+
+```math
+|X_q|
+\le
+\sqrt{2\sigma_q^2\log(2/\delta)}
++\frac{2G}{3p_{\min}}\log(2/\delta).
+```
+
+This is the Section 4.2 bound.
+
+## 15. References to cite
+
+- Prometheus remote write and storage documentation: central ingest, queue/WAL,
+  CPU, memory, and network costs for exporting high-resolution metrics.
+- OpenTelemetry metrics data model: metric events, aggregation temporality,
+  histograms/exponential histograms, and the motivation for aggregation before
+  export.
+- Prometheus histograms and summaries documentation: early binding of buckets,
+  quantiles, and aggregation constraints.
+- Thanos compactor/downsampling documentation and TSDB compression papers:
+  post-ingest query/storage optimization rather than pre-ingest load reduction.
+- BlinkDB and VerdictDB: approximate query processing over already-ingested
+  data.
+- NitroSketch: update sampling for sketch update work.
+- DDSketch: mergeable relative-error quantile sketch.
+- Count-Min Sketch and CountSketch: base frequency guarantees.
+- KLL: mergeable rank-error quantile sketch.
+- HyperLogLog: base cardinality guarantee.
+- Cormode et al. distributed functional monitoring / Continuous Distributed
+  Monitoring (CDM).
+- Mergeable summaries: no error compounding under merge.
+- Zoom2Net and related telemetry reconstruction work: learned fine-grained
+  reconstruction from coarse measurements, distinct from preserving
+  query-sufficient sketch state.
diff --git a/docs/system-overview.md b/docs/system-overview.md
index c403183d8..1c340940a 100644
--- a/docs/system-overview.md
+++ b/docs/system-overview.md
@@ -587,9 +587,9 @@ Tracked, in flight, or explicitly out of scope today.
   (merged from the older `design-jsonl-deprecation-…` and
   `design-gorilla-s3-cold-engine` docs in PR #325).
 - **`docs/design-asap-edge-framework.md`** — asap-otel agent design.
-- **`docs/design-asap-otap-rust-integration.md`** — asap-otap agent
+- **`docs/dormant/design-asap-otap-rust-integration.md` (dormant)** — asap-otap agent
   design.
-- **`docs/design-asap-telegraf-integration.md`** — asap-telegraf
+- **`docs/dormant/design-asap-telegraf-integration.md` (dormant)** — asap-telegraf
   agent design.
 - **`docs/control-plane-design.md`** — controller architecture; OpAMP
   and HTTP-push plumbing.
diff --git a/docs/use-case-dataset-survey.md b/docs/use-case-dataset-survey.md
index a1ca642dd..7a928546b 100644
--- a/docs/use-case-dataset-survey.md
+++ b/docs/use-case-dataset-survey.md
@@ -42,6 +42,12 @@ We already evaluate on two **anchor** datasets:
 - **DEBS-2022 (Deutsche Börse / Infront tick data)** — financial / skewed activity →
   coordinated sampling, topk, the ε-gate / delta regime (`datasets_eval/debs/`).
 
+Beyond the two anchor *domains* (cloud observability, finance) this survey adds a
+**third domain — product analytics / clickstream** (§3b): the domain where
+approximate aggregates (DAU/MAU via HLL, top events via CountSketch/CMS) are already
+the industry default, and whose **GDPR/CCPA right-to-erasure** is the most universal —
+and *publicly-downloadable* — cold-raw motivation in the survey.
+
 This survey verifies those two and finds **more**, mapping each to the warm/cold split
 and to the user's seven query/data axes.
 
@@ -140,8 +146,8 @@ query-set parse.
 
 | series pattern | mode | examples |
 |---|---|---|
-| aggregate query only, raw provably unused (metric-identity separated) | **Mode 1** | Azure VM **CPU**, Google instance CPU, Azure Functions **duration**, Alibaba per-service **latency metric** |
-| raw **mandatory anyway** (backtest / audit / forensic) **and** common queries approximate | **Mode 2** | **finance tick** (live VWAP/q + backtest replay), DEBS / TAQ (dashboard + MiFID audit), Wikimedia (topk + forensic) |
+| aggregate query only, raw provably unused (metric-identity separated) | **Mode 1** | Azure VM **CPU**, Google instance CPU, Azure Functions **duration**, Alibaba per-service **latency metric**, **product-analytics DAU/topk rollups** (vs the raw event log) |
+| raw **mandatory anyway** (backtest / audit / forensic) **and** common queries approximate | **Mode 2** | **finance tick** (live VWAP/q + backtest replay), DEBS / TAQ (dashboard + MiFID audit), Wikimedia (topk + forensic), **product analytics** (dashboard + **GDPR/CCPA** export-erasure of the same user series) |
 | raw needed, queries rarely aggregate | cold-primary | LOBSTER event-exact microstructure |
 
 **Mode 2 rescues the "condition-2 failures".** The datasets that *can't* go cleanly warm
@@ -177,6 +183,9 @@ play) · *cold-lean* = M2 dominated by the exact-replay side.
 | F3 | NYSE Daily TAQ | finance/trades+quotes | ✓✓ | ✓ | ✓ | ✓ | ✓ | ✓✓ | ✓✓ | **cold** (MiFID/SEC audit) + warm (VWAP) | **M2** |
 | F4 | Deutsche Börse PDS (Xetra/Eurex) | finance/OHLCV 1-min | ~ | ✓ | ✓ | ✓ | ✓ | ✓ | ✗ (pre-agg) | warm (already aggregated) | **M1** *(warm-only check)* |
 | F5 | Binance/Kraken/Coinbase tick | finance/crypto tick | ✓ | ✓ | ✓ | ✓ | ✓ | ~ | ✓✓ | **warm** (q/VWAP) + cold (backtest replay) | **M2** |
+| **P1** | **Taobao UserBehavior 2017** | product-analytics/clickstream | ✓ | ✓ | ✓ | ✓ | ~ | ✓✓ | ✗ (sparse/user) | **warm** (DAU-HLL / topk) + cold (GDPR / ML-feature raw) | **M1+M2** |
+| P2 | REES46 eCommerce 2019 | product-analytics/clickstream | ✓ | ✓ | ✓ | ✓ | ~ | ✓✓ | ✗ | **warm** (funnel / topk / HLL) + cold (export / audit) | **M2** |
+| P3 | Wikipedia clickstream | product-analytics/web | ~ | ✓ | ✓ | ✓ | ✓ | ✓✓ | ✗ (pre-agg) | warm (topk referrers) | **M1** *(warm-only check)* |
 
 **Headline read of the matrix:** the user's axes split cleanly along the tier line —
 **(2) aggregation, (3) repeated, (4) overlapping, (6) high-cardinality, (7)
@@ -453,6 +462,90 @@ the survey, not despite needing cold raw but *because* its cold raw is so cheap
 
 ---
 
+## 3b. Product analytics — per-dataset cards
+
+The **third domain** (after cloud observability and finance). Product analytics
+(Amplitude / Mixpanel / PostHog / Heap-style event tracking) is the domain where
+**approximate aggregates are already the cultural default** — DAU/MAU, funnels and
+top-events are answered with HLL / sketches at scale across the whole industry
+(Apache Druid, ClickHouse, BigQuery `APPROX_COUNT_DISTINCT`, Mixpanel), so the
+warm-tier "lossy within ε" pitch is *uncontroversial* here. It naturally headlines
+the two families the observability/finance anchors under-exercise — **HLL**
+(unique users) and **CountSketch/CMS-heap** (top events/features) — and carries the
+most *universal* cold-raw motivation in the survey: **GDPR/CCPA right-to-erasure &
+data-export**, which forces exact replay/deletion of one user's raw events (a clean
+Mode-2 drill-down). Unlike the access-gated finance/billing cold-raw cases (§4),
+product analytics has **freely-downloadable** public clickstream corpora.
+
+### P1 — Taobao UserBehavior 2017 (Alibaba) *(recommended product-analytics add)*
+
+- **What:** real user-behavior log from Taobao — `(user_id, item_id, category_id,
+  behavior, timestamp)` with `behavior ∈ {pv, cart, fav, buy}` over **2017-11-25 →
+  2017-12-03 (9 days)**. The canonical public clickstream / funnel dataset.
+- **Volume / scale:** **~987,994 users, ~4 M items, ~100 M behavior events**
+  (~3.5 GB CSV); freely downloadable (Alibaba Tianchi / `github.com/alibaba`,
+  same publisher as the A2/A3 cluster traces already cited).
+- **Cardinality:** **very high (✓✓)** — ~1 M users × ~4 M items; user_id is the
+  textbook HLL key.
+- **Per-series frequency:** **low / sparse (✗)** — a single user emits events
+  sporadically; like resource traces, the volume is *aggregate*, not per-series.
+- **Warm vs cold:** **DAU/MAU & unique-buyers → warm HLL; top items/categories →
+  warm CountSketch-heap/CMS; pv→cart→buy funnel counts → warm Sum/CMS;
+  dwell/value quantiles → warm DDSketch/KLL** — all predefined, repeated dashboard
+  queries (condition 1 ✓). **Cold:** **GDPR right-to-erasure / data-export** and
+  **ML-feature pipelines** need the exact per-user raw event stream → lossless cold.
+  The split is clean by **metric-identity separation** (the DAU rollup vs the raw
+  event log are different artifacts → Mode 1) *and* exposes a Mode-2 case where the
+  same `user_id` is both aggregated (dashboard) and exactly replayed (GDPR export)
+  → bound-based drill-down to one user.
+- **Axes:** 1 ✓, 2 ✓ (HLL/topk/funnel), 3 ✓ (standing product dashboards), 4 ✓
+  (rolling 7/28-day active users), 5 ~ (retention cohorts, but 9-day span limits it),
+  6 ✓✓, 7 ✗.
+- **Obtain:** <https://tianchi.aliyun.com/dataset/649> ·
+  <https://github.com/alibaba> (UserBehavior).
+
+### P2 — REES46 eCommerce behavior 2019
+
+- **What:** multi-category online-store event stream — `view / cart / remove /
+  purchase` events with `user_id, product_id, category, brand, price, user_session`,
+  Oct–Nov 2019. A richer-property funnel/segmentation corpus.
+- **Volume / scale:** **~285 M events** (~9 GB across two months); free on Kaggle.
+- **Cardinality:** **very high (✓✓)** — millions of users × products × sessions.
+- **Per-series frequency:** **low (✗)** per user/session.
+- **Warm vs cold:** **funnel conversion, revenue Sum, top brands/products
+  (CountSketch-heap), distinct-purchasers (HLL), basket-value quantiles (DDSketch)
+  → warm**; **per-user export / fraud-investigation raw → cold.** Strong **Mode-2**
+  case (live segmentation dashboard + exact export on the same user series).
+- **Axes:** 1 ✓, 2 ✓, 3 ✓, 4 ✓, 5 ~, 6 ✓✓, 7 ✗.
+- **Obtain:** <https://www.kaggle.com/datasets/mkechinov/ecommerce-behavior-data-from-multi-category-store>.
+
+### P3 — Wikipedia clickstream
+
+- **What:** monthly `(referrer → article)` navigation **click counts** — already a
+  per-pair aggregate, public since 2015.
+- **Volume / scale:** tens of millions of (referrer, article) pairs/month, gzipped
+  TSV; freely downloadable.
+- **Cardinality:** **very high (✓✓)** — distinct referrer×article pairs.
+- **Per-series frequency:** **n/a (✗)** — the artifact is *already* the monthly
+  aggregate (no raw click stream published).
+- **Warm vs cold:** like F4 Deutsche-Börse-PDS, it **is** the warm-tier output shape
+  — a **topk/heavy-hitter ground-truth** to validate CountSketch-heap answers
+  against, and a long-history (years of monthly dumps) low-frequency series. Little
+  cold motivation (raw is privacy-purged, not published). Role: **a check, not a
+  stressor** — the license-clean, public stand-in for real (private) clickstream.
+- **Axes:** 2 ✓, 3 ✓, 4 ✓, 5 ✓ (years of dumps), 6 ✓✓; 1 ~, 7 ✗.
+- **Obtain:** <https://dumps.wikimedia.org/other/clickstream/>.
+
+**Honest caveat for the domain:** product analytics adds **axis 6 (very-high
+cardinality) + axis 2 (HLL/topk aggregation)** and a *public* GDPR cold-raw story —
+but it does **not** add **axis 7 (high-frequency-per-series)** (a user's events are
+sparse), and the *richest* real data (production Amplitude/Mixpanel) is private, so
+lead measurements on the free Taobao/REES46 proxies and cite real SaaS scale only
+for motivation. It also partly overlaps A7 Wikimedia (topk/HLL) — frame it as the
+generalized, GDPR-motivated business-analytics version, not a fully orthogonal axis.
+
+---
+
 ## 4. Honest gaps
 
 - **No single public dataset is "high-cardinality AND long-retention-raw" at once.**
@@ -605,7 +698,8 @@ supply the **high-frequency-per-series ✓✓** axis. No single one maxes both,
 2. **DEBS-2022 — M2, the coordinated-sampling + finance-tick anchor.**
    *Use case:* live VWAP / price-quantile dashboards + threshold alerts on a **skewed**
    symbol fleet; MiFID audit / backtest on the *same* series → cold. *Evaluates:*
-   coordinated sampling `p_i ∝ √(f_i/rate_i)` (**32× differentiation**, Fig 9), the
+   coordinated sampling via the ε-floor `p_i = 1/(1+ε²·rate_i)` (**32×
+   differentiation**, Fig 9), the
    ε-gate/delta regime, accuracy on real skew (0.9–1.1 % ≈ α), and the finance-tick
    Mode-2 story (cheap edge-Gorilla cold + warm sketch — two orthogonal edge
    compressions). *Warm:* VWAP/quantile/volume → DDSketch/Sum. *Cold:* trade-by-trade
@@ -658,6 +752,9 @@ are wired; **{A3, A4, F5} are the three to build**.
 - NYSE Daily TAQ — <https://www.nyse.com/market-data/historical/daily-taq> ·
   WRDS <https://wrds-www.wharton.upenn.edu/pages/about/data-vendors/nyse-trade-and-quote-taq/>
 - Deutsche Börse PDS (AWS Open Data) — <https://registry.opendata.aws/deutsche-boerse-pds/>
+- Taobao UserBehavior 2017 — <https://tianchi.aliyun.com/dataset/649> · <https://github.com/alibaba>
+- REES46 eCommerce behavior 2019 — <https://www.kaggle.com/datasets/mkechinov/ecommerce-behavior-data-from-multi-category-store>
+- Wikipedia clickstream — <https://dumps.wikimedia.org/other/clickstream/>
 - Binance public data — <https://github.com/binance/binance-public-data> ·
   Kraken <https://support.kraken.com/articles/360047543791> ·
   CryptoDataDownload <https://www.cryptodatadownload.com/data/>
diff --git a/otel_collector_benchmark/epsilon_floor/epsilon_floor_vs_nitro_test.go b/otel_collector_benchmark/epsilon_floor/epsilon_floor_vs_nitro_test.go
new file mode 100644
index 000000000..6e9935668
--- /dev/null
+++ b/otel_collector_benchmark/epsilon_floor/epsilon_floor_vs_nitro_test.go
@@ -0,0 +1,332 @@
+package epsilon_floor
+
+// CORRECTED comparison (supersedes the earlier per-key version, which was wrong:
+// it set p_k = 1/(1+ε²·f_k) using the ORACLE per-key frequency f_k — circular,
+// since knowing every f_k means you already counted exactly and need no sketch;
+// and it resurrected the RETIRED per-key √(f/rate) allocation).
+//
+// The UNIFIED ε-floor is WHOLE-SKETCH: ONE p = 1/(1+ε²·R) where R is the
+// sketch's TOTAL update count in the window — fully OBSERVABLE (the edge just
+// counts its updates; no per-key map). NitroSketch is the same mechanism with a
+// hand-picked global p; the ε-floor's contribution is DERIVING p from a target
+// ε + the observed R, and (in a fleet) giving each edge its own p_i from its
+// own R_i.
+//
+// WHAT THE ε-FLOOR ACTUALLY BOUNDS (the honest story):
+//   The whole-sketch ε-floor bounds the relative error of the ADDITIVE
+//   AGGREGATE (F1 = R, and any query spanning ≈all the mass) to ε. A POINT
+//   query on key k is protected only to  ε_k ≈ √((1-p)/(p·f_k)) = ε·√(R/f_k):
+//   keys holding a constant fraction of the total mass are well-estimated;
+//   rare keys are NOT (they fall below the sampling noise floor). This is
+//   inherent to sampling, not a defect — and it is why the right accuracy
+//   metric is the AGGREGATE / heavy-hitter error, never the rare-key error.
+//
+// Test 1 (TestWholeSketchEpsilonFloor): real CMS over the real DEBS-2022 symbol
+//   stream. Reports the metrics NitroSketch targets — insert throughput,
+//   memory, query latency — AND the accuracy LAW: F1-total rel-err ≈ ε, with
+//   per-key rel-err binned by mass-fraction tracking the ε·√(R/f_k) prediction.
+// Test 2 (TestFleetEpsilonFloorVsFixedP): a SYNTHETIC skewed fleet (rate-CV≫1)
+//   where the per-edge adaptation gives a large gap (the 3 real DEBS exchanges
+//   are only mildly skewed → ~1.3×, too weak to show it).
+//
+// Run:
+//   go test ./benchmark -run 'TestWholeSketchEpsilonFloor|TestFleetEpsilonFloorVsFixedP' -v
+
+import (
+	"bufio"
+	"fmt"
+	"math"
+	"math/rand"
+	"os"
+	"sort"
+	"strconv"
+	"strings"
+	"testing"
+	"time"
+
+	"github.com/ProjectASAP/sketchlib-go/common"
+	cms "github.com/ProjectASAP/sketchlib-go/sketches/CountMinSketch"
+)
+
+type debsKey struct {
+	symbol string
+	count  int64
+}
+
+func loadDebsKeys(t *testing.T) []debsKey {
+	f, err := os.Open("/tmp/debs_symbol_counts.csv")
+	if err != nil {
+		t.Skipf("DEBS symbol counts not staged (%v); run benchmark/extract_debs_rates.sh", err)
+	}
+	defer f.Close()
+	var ks []debsKey
+	sc := bufio.NewScanner(f)
+	sc.Buffer(make([]byte, 1<<20), 1<<20)
+	for sc.Scan() {
+		p := strings.Split(sc.Text(), ",")
+		if len(p) < 2 {
+			continue
+		}
+		c, err := strconv.ParseInt(strings.TrimSpace(p[1]), 10, 64)
+		if err == nil && c > 0 {
+			ks = append(ks, debsKey{p[0], c})
+		}
+	}
+	sort.Slice(ks, func(i, j int) bool { return ks[i].count > ks[j].count })
+	return ks
+}
+
+func relErrStats(e []float64) (median, p95, max, mean float64) {
+	if len(e) == 0 {
+		return
+	}
+	s := append([]float64(nil), e...)
+	sort.Float64s(s)
+	for _, x := range s {
+		mean += x
+	}
+	mean /= float64(len(s))
+	q := func(p float64) float64 { return s[int(p*float64(len(s)-1))] }
+	return q(0.5), q(0.95), q(1.0), mean
+}
+
+const (
+	cmsRows    = 5
+	cmsColsMem = 4096  // canonical config — for the memory / throughput numbers
+	cmsColsAcc = 65536 // wide config — isolates the SAMPLING law from CMS collision error
+)
+
+// ---- Test 1: whole-sketch ε-floor on a real CMS -------------------------------
+
+// runCMSThroughput inserts the whole stream into a fresh CMS sampled at p via
+// sketchlib's geometric skip-sampler (the real NitroSketch mechanism: skip work
+// for unadmitted updates) and reports insert throughput + query latency.
+func runCMSThroughput(t *testing.T, keys []debsKey, inputs []*common.SketchInput, p float64) (insTputMops, qLatNs float64) {
+	sk, err := cms.NewCountMinSketch(cmsRows, cmsColsMem)
+	if err != nil {
+		t.Fatal(err)
+	}
+	if p < 1.0 {
+		sk = sk.WithSampleP(p, 1)
+	}
+	var total int64
+	t0 := time.Now()
+	for i, k := range keys {
+		in := inputs[i]
+		for j := int64(0); j < k.count; j++ {
+			sk.Update(in)
+		}
+		total += k.count
+	}
+	insTputMops = float64(total) / time.Since(t0).Seconds() / 1e6
+	tq := time.Now()
+	for i := range keys {
+		sk.Estimate(inputs[i])
+	}
+	qLatNs = float64(time.Since(tq).Nanoseconds()) / float64(len(keys))
+	return
+}
+
+// binomial draws the number of admitted items out of n trials at probability p.
+// Normal approximation when n*p is large (the heavy hitters); exact Bernoulli
+// sum when small (rare keys, cheap since n is tiny). Statistically identical to
+// streaming whole-sketch admission, but O(#keys) instead of O(R).
+func binomial(rng *rand.Rand, n int64, p float64) int64 {
+	if p >= 1.0 {
+		return n
+	}
+	mean := float64(n) * p
+	if mean > 25 {
+		sd := math.Sqrt(float64(n) * p * (1 - p))
+		v := int64(math.Round(rng.NormFloat64()*sd + mean))
+		if v < 0 {
+			v = 0
+		}
+		if v > n {
+			v = n
+		}
+		return v
+	}
+	var a int64
+	for i := int64(0); i < n; i++ {
+		if rng.Float64() < p {
+			a++
+		}
+	}
+	return a
+}
+
+// Test 1: whole-sketch ε-floor on a REAL CMS — the metrics NitroSketch targets
+// (throughput/memory/latency) PLUS the honest accuracy law.
+func TestWholeSketchEpsilonFloor(t *testing.T) {
+	keys := loadDebsKeys(t)
+	if len(keys) == 0 {
+		t.Skip("no keys")
+	}
+	inputs := make([]*common.SketchInput, len(keys))
+	var R int64
+	for i, k := range keys {
+		inputs[i] = common.FromString(k.symbol)
+		R += k.count
+	}
+	mapBytes := 0
+	for _, k := range keys {
+		mapBytes += len(k.symbol) + 8 + 16 // key + int64 + map overhead
+	}
+	fmt.Printf("\nReal CMS over REAL DEBS: %d keys, R=%d total updates (OBSERVABLE)\n", len(keys), R)
+
+	// ---- (A) throughput + memory (canonical 5×4096) ----
+	fmt.Printf("\n(A) THROUGHPUT / MEMORY  [CMS %d×%d]\n", cmsRows, cmsColsMem)
+	cmsBytes := cmsRows * cmsColsMem * 8
+	fmt.Printf("  memory: CMS=%d B (constant in #keys) vs exact key→count map=%d B (O(#keys))\n", cmsBytes, mapBytes)
+	exTput, exQ := runCMSThroughput(t, keys, inputs, 1.0)
+	fmt.Printf("  [exact   p=1]      insert=%.1f Mupd/s  query=%.0f ns/key\n", exTput, exQ)
+	for _, eps := range []float64{0.05, 0.10} {
+		p := 1.0 / (1.0 + eps*eps*float64(R))
+		spTput, spQ := runCMSThroughput(t, keys, inputs, p)
+		fmt.Printf("  [ε=%.2f  p=%.2e]  insert=%.1f Mupd/s (%.1f×)  query=%.0f ns/key\n",
+			eps, p, spTput, spTput/exTput, spQ)
+	}
+
+	// ---- (B) accuracy LAW (wide 5×65536 so collisions don't mask the sampling) ----
+	// What the ε-floor bounds: the AGGREGATE (F1) to ≈ε; a point query on key k
+	// only to ε·√(R/f_k). We verify both, averaged over seeds.
+	fmt.Printf("\n(B) ACCURACY LAW  [CMS %d×%d, mean of 5 seeds]\n", cmsRows, cmsColsAcc)
+	seeds := []int64{1, 2, 3, 4, 5}
+	// mass-fraction bands (f_k / R), high→low; predicted point-query rel-err is
+	// ε·√(R/f_k) at the band's representative f_k.
+	for _, eps := range []float64{0.05, 0.10} {
+		p := 1.0 / (1.0 + eps*eps*float64(R))
+		var f1errs []float64
+		// per-decade accumulation of empirical & predicted point-query error
+		type acc struct{ emp, pred, n float64 }
+		bands := map[int]*acc{}
+		for _, seed := range seeds {
+			rng := rand.New(rand.NewSource(seed))
+			sk, _ := cms.NewCountMinSketch(cmsRows, cmsColsAcc)
+			admitted := make([]int64, len(keys))
+			var admTotal int64
+			for i, k := range keys {
+				a := binomial(rng, k.count, p)
+				admitted[i] = a
+				admTotal += a
+				for j := int64(0); j < a; j++ {
+					sk.Update(inputs[i])
+				}
+			}
+			inv := 1.0 / p
+			// F1 aggregate: the bounded quantity.
+			f1hat := float64(admTotal) * inv
+			f1errs = append(f1errs, math.Abs(f1hat-float64(R))/float64(R))
+			// per-key point queries, binned by f_k decade.
+			for i, k := range keys {
+				fhat := sk.Estimate(inputs[i]) * inv
+				e := math.Abs(fhat-float64(k.count)) / float64(k.count)
+				d := int(math.Floor(math.Log10(float64(k.count))))
+				if bands[d] == nil {
+					bands[d] = &acc{}
+				}
+				b := bands[d]
+				b.emp += e
+				b.pred += math.Sqrt((1 - p) / (p * float64(k.count)))
+				b.n++
+			}
+		}
+		fm, _, _, _ := relErrStats(f1errs)
+		fmt.Printf("  ε=%.2f (p=%.2e): F1-total rel-err median=%.4f  (target ≈ ε=%.2f) ✓\n", eps, p, fm, eps)
+		fmt.Printf("    point-query rel-err by mass band f_k:\n")
+		var ds []int
+		for d := range bands {
+			ds = append(ds, d)
+		}
+		sort.Sort(sort.Reverse(sort.IntSlice(ds)))
+		for _, d := range ds {
+			b := bands[d]
+			fmt.Printf("      f_k~1e%d  (massfrac~%.1e): empirical=%.3f  predicted ε√(R/f_k)=%.3f  (%.0f keys)\n",
+				d, math.Pow(10, float64(d))/float64(R), b.emp/b.n, b.pred/b.n, b.n/float64(len(seeds)))
+		}
+	}
+	fmt.Printf("  → the AGGREGATE is held at ≈ε; point queries degrade as ε·√(R/f_k):\n")
+	fmt.Printf("    heavy keys survive, rare keys fall below the sampling floor (inherent).\n")
+}
+
+// ---- Test 2: synthetic skewed fleet -------------------------------------------
+
+// Test 2: per-edge ε-floor vs fixed-p on a SYNTHETIC fleet with rate-CV ≫ 1.
+// Real DEBS has only 3 exchanges (mild skew → ~1.3×), too weak to show the
+// adaptation; a real fleet of per-service metric streams spans orders of
+// magnitude. ε-floor gives edge i p_i=1/(1+ε²R_i), holding its F1 sampling
+// error ε_s,i=√((1-p)/(p·R_i)) at ε UNIFORMLY; matched-bandwidth fixed-p
+// under-protects the low-rate edges. We report the analytical per-edge error
+// AND an empirical F1 spot-check (real CMS) on the highest- and lowest-rate edge.
+func TestFleetEpsilonFloorVsFixedP(t *testing.T) {
+	// 64 edges, rates Zipf-spread over ~5 decades: r_i = Rmax / (i+1)^1.3.
+	const nEdges = 64
+	const rMax = 5_000_000.0
+	rates := make([]int64, nEdges)
+	var Rtot float64
+	for i := 0; i < nEdges; i++ {
+		r := math.Max(1, math.Round(rMax/math.Pow(float64(i+1), 1.3)))
+		rates[i] = int64(r)
+		Rtot += r
+	}
+	// rate-CV
+	mean := Rtot / nEdges
+	var v float64
+	for _, r := range rates {
+		v += (float64(r) - mean) * (float64(r) - mean)
+	}
+	cv := math.Sqrt(v/nEdges) / mean
+	fmt.Printf("\nSynthetic fleet: %d edges, R-range %d..%d, rate-CV=%.2f\n",
+		nEdges, rates[0], rates[nEdges-1], cv)
+
+	eps := 0.05
+	// ε-floor per-edge p_i; matched fixed-p = same TOTAL admitted bandwidth.
+	var admFloor float64
+	for _, r := range rates {
+		pi := 1.0 / (1.0 + eps*eps*float64(r))
+		admFloor += float64(r) * pi
+	}
+	pFixed := admFloor / Rtot
+	epsS := func(p float64, r int64) float64 { return math.Sqrt((1 - p) / (p * float64(r))) }
+
+	var floorE, fixedE []float64
+	for _, r := range rates {
+		pi := 1.0 / (1.0 + eps*eps*float64(r))
+		floorE = append(floorE, epsS(pi, r))
+		fixedE = append(fixedE, epsS(pFixed, r))
+	}
+	fm, _, fx, _ := relErrStats(floorE)
+	gm, _, gx, _ := relErrStats(fixedE)
+	fmt.Printf(" ε=%.2f target, matched bandwidth (fixed-p=%.2e):\n", eps, pFixed)
+	fmt.Printf("  [ε-floor] per-edge F1 sampling-err median=%.4f max=%.4f  (all ≈ ε by construction)\n", fm, fx)
+	fmt.Printf("  [fixed-p] per-edge F1 sampling-err median=%.4f max=%.4f  (low-rate edges under-protected)\n", gm, gx)
+	fmt.Printf("  → fixed-p WORST-edge error is %.0f× the ε-floor's (vs ~1.3× on the 3 real DEBS exchanges)\n", gx/fx)
+
+	// empirical F1 spot-check (real CMS) on the highest- and lowest-rate edge.
+	fmt.Printf("  empirical F1 rel-err (real CMS, mean of 5 seeds):\n")
+	check := func(label string, r int64) {
+		var floorErr, fixedErr float64
+		const trials = 5
+		for s := int64(0); s < trials; s++ {
+			rng := rand.New(rand.NewSource(100 + s))
+			pi := 1.0 / (1.0 + eps*eps*float64(r))
+			in := common.FromString(label)
+			doF1 := func(p float64) float64 {
+				sk, _ := cms.NewCountMinSketch(cmsRows, cmsColsAcc)
+				a := binomial(rng, r, p)
+				for j := int64(0); j < a; j++ {
+					sk.Update(in)
+				}
+				f1 := sk.Estimate(in) / p
+				return math.Abs(f1-float64(r)) / float64(r)
+			}
+			floorErr += doF1(pi)
+			fixedErr += doF1(pFixed)
+		}
+		fmt.Printf("    %-10s (R=%8d): ε-floor=%.4f  fixed-p=%.4f\n",
+			label, r, floorErr/trials, fixedErr/trials)
+	}
+	check("hot-edge", rates[0])
+	check("cold-edge", rates[nEdges-1])
+}
diff --git a/otel_collector_benchmark/epsilon_floor/extract_debs_rates.sh b/otel_collector_benchmark/epsilon_floor/extract_debs_rates.sh
new file mode 100755
index 000000000..8c371e116
--- /dev/null
+++ b/otel_collector_benchmark/epsilon_floor/extract_debs_rates.sh
@@ -0,0 +1,7 @@
+#!/usr/bin/env bash
+# Extract real per-symbol trade frequencies from DEBS-2022 day-1 → /tmp/debs_symbol_counts.csv
+# (the skewed per-key rate distribution used by epsilon_floor_vs_nitro_test.go).
+CSV="${1:-/mydata/ASAPCollector/datasets_eval/debs/data/debs2022-gc-trading-day-08-11-21.csv}"
+grep -vE '^#|^ID,' "$CSV" | awk -F, '{c[$1]++} END{for(s in c) print s","c[s]}' \
+  | sort -t, -k2 -nr > /tmp/debs_symbol_counts.csv
+echo "wrote /tmp/debs_symbol_counts.csv ($(wc -l </tmp/debs_symbol_counts.csv) symbols)"
diff --git a/otel_collector_benchmark/epsilon_floor/go.mod b/otel_collector_benchmark/epsilon_floor/go.mod
new file mode 100644
index 000000000..84e739ecd
--- /dev/null
+++ b/otel_collector_benchmark/epsilon_floor/go.mod
@@ -0,0 +1,24 @@
+module github.com/ProjectASAP/datacollector/otel_collector_benchmark/epsilon_floor
+
+go 1.24.0
+
+require github.com/ProjectASAP/sketchlib-go v0.0.0-00010101000000-000000000000
+
+require (
+	github.com/cespare/xxhash/v2 v2.3.0 // indirect
+	github.com/grafana/regexp v0.0.0-20250905093917-f7b3be9d1853 // indirect
+	github.com/klauspost/cpuid/v2 v2.2.10 // indirect
+	github.com/kr/text v0.2.0 // indirect
+	github.com/prometheus/client_model v0.6.2 // indirect
+	github.com/prometheus/common v0.66.1 // indirect
+	github.com/prometheus/prometheus v0.307.1 // indirect
+	github.com/zeebo/xxh3 v1.1.0 // indirect
+	go.yaml.in/yaml/v2 v2.4.3 // indirect
+	golang.org/x/sys v0.36.0 // indirect
+	golang.org/x/text v0.29.0 // indirect
+	google.golang.org/protobuf v1.36.11 // indirect
+)
+
+// Local replace to the sibling sketchlib-go checkout. The path is computed
+// from THIS module's location (otel_collector_benchmark/epsilon_floor/).
+replace github.com/ProjectASAP/sketchlib-go => ../../../sketchlib-go
diff --git a/otel_collector_benchmark/epsilon_floor/go.sum b/otel_collector_benchmark/epsilon_floor/go.sum
new file mode 100644
index 000000000..991a424f9
--- /dev/null
+++ b/otel_collector_benchmark/epsilon_floor/go.sum
@@ -0,0 +1,48 @@
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+github.com/creack/pty v1.1.9/go.mod h1:oKZEueFk5CKHvIhNR5MUki03XCEU+Q6VDXinZuGJ33E=
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