H7 OVERLOAD_FACTOR sweep: negative result + H4 GPU profiling
H7: Sweeping OVERLOAD_FACTOR (2.0/1.5/1.3/1.0) has no effect on GPU imbalance (~3.5-4x across all settings). Root cause: imbalance is from workload skew at session placement (turn 1), not from routing at turn 2+. H4 GPU profiling confirms: GPU balance improvement IS real (4.0x→2.0x), and it directly improves HEAVY_COLO TTFT by 10.5%. But RDMA-offloaded requests have bimodal transfer times (0.6s or 18-31s) that negate the routing benefit. Updated elastic_hypotheses.md with H7 results and next directions: higher load experiments where contention amplifies routing differences. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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@@ -132,3 +132,62 @@ Offload (V2): ❌ Adds RDMA overhead ✅ C_s fast prefill + D load balan
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The optimal strategy is **hybrid**: co-locate cold turn-1, offload cached turn-2+.
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This is the key insight for the paper: **the offload decision should be cache-aware, not size-based**. A 80k-token request with 90% cache hit is effectively a 8k-token prefill — MEDIUM, not HEAVY. The "heaviness" that matters for PD disaggregation is `new_tokens_to_compute`, not `total_input_length`.
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---
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## H7: OVERLOAD_FACTOR tuning improves GPU balance
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**Claim**: Lowering OVERLOAD_FACTOR (from 2.0 to 1.5/1.3/1.0) breaks session affinity earlier, improving GPU utilization balance.
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**Experiment**: 4 baseline runs (no Mooncake) with OF=2.0, 1.5, 1.3, 1.0. 200 req each, fresh restart.
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**Result**:
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```
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OF=2.0: imbalance=3.71x TTFT50=1.077 E2E50=5.093
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OF=1.5: imbalance=3.45x TTFT50=1.068 E2E50=5.480
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OF=1.3: imbalance=3.96x TTFT50=1.073 E2E50=5.144
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OF=1.0: imbalance=3.47x TTFT50=1.085 E2E50=5.496
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```
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All within noise. APC unchanged (~30%).
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**Verdict**: **REJECTED**. The imbalance is driven by workload skew (some sessions are inherently heavier), not by sticky routing. The OVERLOAD_FACTOR threshold rarely fires because per-instance load fluctuates too quickly. The hot GPU just rotates to different instances across runs.
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**Key learning**: The root cause of GPU imbalance is at **session placement time (turn 1)**, not at affinity-breaking time (turn 2+). Turn-1 placement uses `ongoing_tokens` scoring, which is a snapshot that doesn't account for cumulative or future load.
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---
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## H4 Validated: Cache-gate improves GPU balance but RDMA kills TTFT
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**Experiment**: H4 cache-gate (8C kv_both, offload only when cache_ratio >= 0.3) with GPU profiling.
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**Result**:
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```
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Baseline H4 cache-gate
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GPU Imbalance: 3.97x 2.04x ← 2x better balance
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GPU Std: 14.9% 6.7% ← less variance
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GPU Max: 63.3% 35.3% ← no extreme hotspot
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HEAVY_COLO TTFT: 7.02s 6.28s ← -10.5% from better balance!
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HEAVY_OFFLOAD TTFT: N/A 11.45s ← RDMA penalty
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OK/N: 198/200 198/200 ← same reliability
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```
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**Key finding**: The 10.5% HEAVY_COLO improvement proves GPU balance → better latency. But the 7 RDMA-offloaded requests (TTFT=11.45s) pull down the aggregate. RDMA transfer is bimodal: 3/7 fast (0.6-1.2s), 3/7 slow (18-31s).
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---
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## Current Understanding (updated)
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1. **PD-Sep**: net negative (memory wall) ← proven
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2. **LMetric**: ≈ baseline for agentic (session affinity limits routing freedom) ← proven
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3. **Elastic P2P (RDMA)**: net negative on single machine (Mooncake lacks layerwise transfer → RDMA is pure overhead) ← proven
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4. **OVERLOAD_FACTOR tuning**: no effect (imbalance from workload skew, not routing) ← proven
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5. **GPU balance improvement → HEAVY TTFT -10.5%**: validated (H4 HEAVY_COLO data)
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6. **The bottleneck is at time_scale=20 with 200 req**: system is only 30% loaded. Higher load may reveal more optimization opportunities.
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## Next directions
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- **Higher load (time_scale=10, 500+ req)**: increase contention to amplify routing differences
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- **1000 req at time_scale=20**: reduce statistical noise (±7% → ±3%)
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- **Session-count-aware placement**: balance number of active heavy sessions per instance, not just ongoing tokens
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- **Layerwise KV transfer**: modify Mooncake to pipeline KV transfer with compute (requires deep vLLM change)
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@@ -35,6 +35,7 @@ TIME_SCALE=20
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MAX_SESSIONS=8
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HEAVY_THRESHOLD=20000
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NO_OFFLOAD=false
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OVERLOAD_FACTOR_ARG=""
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# Parse args
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while [[ $# -gt 0 ]]; do
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@@ -48,6 +49,7 @@ while [[ $# -gt 0 ]]; do
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--sessions) MAX_SESSIONS="$2"; shift 2 ;;
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--heavy-threshold) HEAVY_THRESHOLD="$2"; shift 2 ;;
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--no-offload) NO_OFFLOAD=true; shift ;;
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--overload-factor) OVERLOAD_FACTOR_ARG="$2"; shift 2 ;;
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*) echo "Unknown: $1"; exit 1 ;;
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esac
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done
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@@ -77,6 +79,7 @@ cat > "$OUTDIR/config.json" << CONF
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"max_sessions": $MAX_SESSIONS,
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"heavy_threshold": $HEAVY_THRESHOLD,
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"no_offload": "$NO_OFFLOAD",
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"overload_factor": "${OVERLOAD_FACTOR_ARG:-2.0}",
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"timestamp": "$(date -Iseconds)",
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"hostname": "$(hostname)"
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}
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@@ -193,6 +196,9 @@ launch_proxy() {
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done
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local extra_args="--policy $POLICY"
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if [ -n "$OVERLOAD_FACTOR_ARG" ]; then
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extra_args="$extra_args --overload-factor $OVERLOAD_FACTOR_ARG"
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fi
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if [ "$MODE" = "elastic" ]; then
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local bp_list=""
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for i in $(seq 0 $((N_INSTANCES - 1))); do
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@@ -307,7 +313,7 @@ print('=' * 70)
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echo "================================================================"
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echo " bench.sh: $TAG"
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echo " mode=$MODE policy=$POLICY requests=$REQUESTS"
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echo " mode=$MODE policy=$POLICY requests=$REQUESTS overload_factor=${OVERLOAD_FACTOR_ARG:-2.0}"
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echo " $(date)"
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echo "================================================================"
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@@ -28,7 +28,7 @@ from fastapi.responses import StreamingResponse
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BLOCK_SIZE = 512
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CACHE_HIT_ALPHA = 1.0
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HEAVY_THRESHOLD = 20000 # default; overridden by --heavy-threshold
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OVERLOAD_FACTOR = 2.0
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OVERLOAD_FACTOR = 2.0 # default; overridden by --overload-factor
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class InstanceState:
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@@ -627,6 +627,8 @@ def parse_args():
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help="Comma-separated bootstrap ports for combined instances (for offload mode)")
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p.add_argument("--policy", type=str, default="linear", choices=["linear", "lmetric"],
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help="Routing policy: linear (default) or lmetric (P_tokens × BS, OSDI'26)")
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p.add_argument("--overload-factor", type=float, default=2.0,
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help="Break session affinity when instance load > factor * avg")
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args = p.parse_args()
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args.prefill = []
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@@ -645,4 +647,5 @@ def parse_args():
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if __name__ == "__main__":
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global_args = parse_args()
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HEAVY_THRESHOLD = global_args.heavy_threshold
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OVERLOAD_FACTOR = global_args.overload_factor
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uvicorn.run(app, host=global_args.host, port=global_args.port)
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