Gahow Wang
efe984477a
Balanced session-sticky routing improves APC from 44.7% to 48.7% (+4pp, close to simulated 49.2%) but TTFT worsens by 30% and E2E by 23%. Root cause: session-sticky creates load hotspots — some instances get multiple heavy concurrent sessions, causing queue delays, despite higher per-instance APC. Key finding: APC optimization and latency optimization are in tension. - Cache affinity (sticky) -> higher APC, worse load balance -> worse latency - Load-based routing (old) -> lower APC, better load balance -> better latency The optimal design must balance both dimensions, not optimize one at the expense of the other. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
51 lines
1.8 KiB
Python
51 lines
1.8 KiB
Python
"""Compare balanced session-sticky routing vs old cache-aware baseline."""
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import json
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def stats(path):
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rows = [json.loads(l) for l in open(path)]
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ok = [r for r in rows if not r.get("error")]
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ttfts = sorted([r["ttft_s"] for r in ok if r.get("ttft_s")])
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tpots = sorted([r["tpot_s"] for r in ok if r.get("tpot_s") and r["tpot_s"]>0])
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lats = sorted([r["latency_s"] for r in ok])
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p = lambda v,q: v[min(int(q*len(v)),len(v)-1)] if v else 0
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return {"ok": len(ok), "n": len(rows),
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"t50": p(ttfts,.5), "t90": p(ttfts,.9),
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"p50": p(tpots,.5), "p90": p(tpots,.9),
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"e50": p(lats,.5), "e90": p(lats,.9)}
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b = stats("outputs/exp2_combined_tp1_dp8/metrics.jsonl")
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n = stats("outputs/balanced_routing/metrics.jsonl")
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print("BALANCED ROUTING vs BASELINE")
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print("Both: 1000 req, TP=1 DP=8 combined, cache-aware scheduler")
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print("=" * 65)
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fmt = "%-28s %7s %8s %8s %8s %8s"
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print(fmt % ("Config", "OK/N", "TTFT50", "TTFT90", "TPOT90", "E2E50"))
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print("-" * 65)
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print(fmt % ("Old cache-aware",
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"%d/%d" % (b["ok"], b["n"]),
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"%.3f" % b["t50"], "%.3f" % b["t90"],
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"%.3f" % b["p90"], "%.3f" % b["e50"]))
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print(fmt % ("Balanced session-sticky",
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"%d/%d" % (n["ok"], n["n"]),
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"%.3f" % n["t50"], "%.3f" % n["t90"],
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"%.3f" % n["p90"], "%.3f" % n["e50"]))
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print()
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print("APC:")
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print(" Old cache-aware: 44.7%%")
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print(" Balanced session-sticky: 48.7%% (+4.0pp)")
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print(" Simulation prediction: 49.2%%")
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print(" Theoretical (infinite): 51.0%%")
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print()
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print("DELTA:")
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for label, bv, av in [
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("TTFT p50", b["t50"], n["t50"]),
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("TTFT p90", b["t90"], n["t90"]),
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("TPOT p90", b["p90"], n["p90"]),
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("E2E p50", b["e50"], n["e50"]),
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]:
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delta = (av/bv - 1) * 100 if bv > 0 else 0
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print(" %s: %.3f -> %.3f (%+.1f%%)" % (label, bv, av, delta))
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