# Milestone Report: Elastic P2P vs PD-Combined Baseline

**Date**: 2026-05-22
**Author**: Gahow Wang
**Status**: Phase 1 complete — baseline + elastic validated, system-level analysis done

---

## 1. Research Question

For agentic LLM workloads (long input, short output, high KV cache reuse), is prefill-decode disaggregation beneficial? If full PD separation hurts (proven in §3), can **selective** disaggregation of only heavy requests improve serving latency while preserving KV cache locality?

## 1.1 Errata / Superseded sections

> This report has been revised several times as the methodology matured.
> The sections below are kept for historical context but their numerical
> conclusions have been **superseded** — do not cite them in isolation.
>
> - **§3.1 (initial PD-sep vs PD-combined)**: ran with the old random
>   sampler + `--time-scale` compression + `--max-inflight-sessions 8`.
>   Cross-session KV reuse dropped from 52% → 16%, and per-GPU concurrency
>   was capped at 1 req/GPU. Superseded by §3.6.
> - **Earlier "elastic v3" warm-vs-fresh runs**: baselines were not
>   restarted between trials, leaving residual KV cache that inflated
>   baseline TTFT ~2×. Superseded by the cold-start results in §3.6/§3.7.
> - **Any reference to running `--max-inflight-sessions 64+`**: that flag
>   was removed when replay moved to trace-driven dispatch. The next-step
>   experiment requires restoring the flag first (see `FIXES.md` §B2
>   route A) before any production-concurrency numbers can be produced.
>
> The authoritative results are in **§3.6 and §3.7**.

## 2. Experimental Setup

### 2.1 Hardware

| Resource | Spec |
|----------|------|
| Machine | dash0 / dash1 (identical config) |
| GPU | 8× NVIDIA H20 96GB HBM, NVLink |
| Network | 4× ConnectX-7 200Gbps RDMA |
| Storage | cpfs shared storage across machines |

### 2.2 Software

| Component | Version | Notes |
|-----------|---------|-------|
| vLLM | 0.18.1 (source in `third_party/vllm/`) | Patched scheduler assert (see `patches/`) |
| Mooncake | 0.3.10 | RDMA-based KV transfer between instances |
| Python | 3.x managed by `uv` | `.venv/` at project root |
| Model | `Qwen3-Coder-30B-A3B-Instruct` | MoE 128 experts top-8, 3B active params |
| Model path | `~/models/Qwen/Qwen3-Coder-30B-A3B-Instruct` | Same on dash0 and dash1 |

### 2.3 Workload Trace

| Property | Value |
|----------|-------|
| Source | GLM-5.1 Agentic Coder, production cluster, 2h window |
| Raw trace | `~/ali-trace/trace-glm5.1-formatted/051315-051317.jsonl` on dash0 |
| Total requests | 2,114,220 |
| Avg input tokens | 33,600 (p50=20k, p90=88k) |
| Avg output tokens | 445 (p50=80) |
| I/O ratio | 75.6× aggregate |
| Prefill token share | 98% |
| KV reuse breakdown | 62% intra-session, 38% cross-session (token-level) |
| Theoretical max APC | 67% (infinite cache, single instance, prefix-only) |

**Sampled trace for benchmarks**: `traces/w600_r0.0015_st30.jsonl` (1214 requests, 688 sessions, 70% multi-turn). Generated with window+thin sampling:

```bash
python scripts/sample_trace.py \
    --input ~/ali-trace/trace-glm5.1-formatted/051315-051317.jsonl \
    --output traces/w600_r0.0015_st30.jsonl \
    --sample-ratio 0.0015 --max-single-turn-ratio 0.3 \
    --window-seconds 600 --seed 42
```

| Trace property | Value |
|---------------|-------|
| Sessions | 688 (70% multi-turn, avg 4.9 turns) |
| Requests | 1214 (use `--request-limit 850` for daily, full for validation) |
| Avg input tokens | 48,776 |
| Trace span | 2912s (48.5 min); dense segment 0-990s (850 req) |
| Peak QPS | 1.6 req/s (in dense segment) |
| Hash block sharing | 48.3% (vs 52% full trace) |
| Theoretical APC | 80% (full), 76% (first 850 req) |

> **Sampling methodology (2026-05-23)**: Prior traces used random session sampling + `--time-scale` compression + `--max-inflight-sessions` semaphore, which (a) destroyed cross-session hash block sharing (52% → 16%), (b) artificially limited concurrency to 1 req/GPU, and (c) masked prefill-decode interference. The new approach uses contiguous time-window sampling with session thinning (`--max-single-turn-ratio 0.3`) to preserve KV reuse patterns, and trace-driven replay with no artificial concurrency limits.

### 2.4 Two Configurations Compared

#### Baseline: PD-Combined (8× TP=1 DP=8)

```
8 independent vLLM instances, 1 GPU each, no Mooncake.
All instances do both prefill and decode.
Global scheduler (cache_aware_proxy.py --combined) handles:
  - Session-sticky routing (multi-turn → same instance)
  - Load-aware override (if pinned instance > 2× avg load, redirect)
  - Cache-hit scoring (prefer instance with matching prefix blocks)
```

Launch:
```bash
# On dash0:
for i in $(seq 0 7); do
    MASTER_PORT=$((29500+i)) CUDA_VISIBLE_DEVICES=$i \
    vllm serve ~/models/Qwen/Qwen3-Coder-30B-A3B-Instruct \
        --port $((8000+i)) --tp 1 \
        --enable-prefix-caching --enforce-eager \
        --gpu-memory-utilization 0.9 --max-model-len 200000 \
        > /tmp/ab_base_$i.log 2>&1 &
done

python scripts/cache_aware_proxy.py \
    --combined http://127.0.0.1:800{0..7} --port 9090
```

#### Elastic P2P Offload (8× TP=1 kv_both + selective offload)

```
8 independent vLLM instances, 1 GPU each, all kv_role=kv_both (Mooncake).
Same global scheduler, plus elastic offload logic:
  - Proxy classifies each request: WARM (<5k new), MEDIUM (5-20k), HEAVY (>20k)
  - WARM/MEDIUM: co-located on session-sticky instance (no KV transfer)
  - HEAVY: prefill on a different instance (P), KV via Mooncake RDMA,
    decode on session-sticky instance (D)
  - Cap: max 4 concurrent offloads (MAX_OFFLOAD_INFLIGHT)
  - P instance selection: round-robin with overload skip
```

Launch:
```bash
# On dash1 (or use scripts/launch_elastic_p2p.sh):
for i in $(seq 0 7); do
    VLLM_MOONCAKE_BOOTSTRAP_PORT=$((8998+i)) \
    MASTER_PORT=$((29500+i)) CUDA_VISIBLE_DEVICES=$i \
    vllm serve ~/models/Qwen/Qwen3-Coder-30B-A3B-Instruct \
        --port $((8000+i)) --tp 1 \
        --enable-prefix-caching --enforce-eager \
        --gpu-memory-utilization 0.9 --max-model-len 200000 \
        --kv-transfer-config '{"kv_connector":"MooncakeConnector","kv_role":"kv_both"}' \
        > /tmp/ab_elastic_$i.log 2>&1 &
    sleep 2  # stagger to avoid NCCL port collision
done

# Wait for bootstrap servers
for bp in $(seq 8998 9005); do
    until curl -s localhost:$bp/query > /dev/null 2>&1; do sleep 2; done
done

python scripts/cache_aware_proxy.py \
    --combined http://127.0.0.1:800{0..7} \
    --bootstrap-ports 8998,8999,9000,9001,9002,9003,9004,9005 \
    --offload --heavy-threshold 20000 --port 9090
```

### 2.5 Benchmark Parameters

| Parameter | Value |
|-----------|-------|
| Trace | `traces/w600_r0.0015_st30.jsonl` (window+thin, 70% multi-turn) |
| Daily iteration | `--request-limit 850` (~13 min, APC≈76%) |
| Full validation | All 1214 requests (~48 min, APC≈80%) |
| Replay mode | Trace-driven (no session limit, no time compression) |
| Request timeout | 600s |
| vLLM flags | `--enforce-eager --enable-prefix-caching --max-model-len 200000` |
| GPU memory util | 0.9 |
| Fresh restart | Both configs started from cold (no warm cache) |

### 2.6 Reproducing the Benchmark

```bash
# Activate environment
cd ~/agentic-kv && source .venv/bin/activate

# Ensure sampled trace exists
python scripts/sample_trace.py \
    --input ~/ali-trace/trace-glm5.1-formatted/051315-051317.jsonl \
    --output traces/sampled_1000req_seed42.jsonl \
    --target-requests 1000 --seed 42

# Run benchmark (daily iteration)
bash scripts/bench.sh --tag my_experiment --mode baseline --policy linear \
    --trace traces/w600_r0.0015_st30.jsonl --requests 850

# Run benchmark (full validation)
bash scripts/bench.sh --tag my_experiment_full --mode baseline --policy linear \
    --trace traces/w600_r0.0015_st30.jsonl
```

## 3. Results

> **Errata (2026-05-22)**: The initial cross-machine A/B (dash0 baseline vs dash1 elastic) reported -44% E2E improvement. Post-hoc analysis revealed the dash0 baseline instances were **not freshly restarted** — residual KV cache from prior experiments caused 2× TTFT inflation.

> **Errata (2026-05-23)**: §3.1 results used artificial concurrency limits (`--max-inflight-sessions 8`, 1 req/GPU) and random session sampling that destroyed cross-session KV sharing (52% → 16%). See §3.6 for production-realistic results with corrected methodology.

### 3.1 Legacy Comparison (artificial 1 req/GPU, 200 req)

| Config | OK/N | TTFT p50 | TTFT p90 | TPOT p90 | E2E p50 |
|--------|------|----------|----------|----------|---------|
| **Baseline (no Mooncake)** | **198/200** | **1.075s** | **9.384s** | **0.076s** | **5.075s** |
| LMetric routing | 198/200 | 1.099s | 9.392s | 0.073s | 5.205s |
| Elastic P2P (kv_both) | 195/200 | 1.018s | 11.312s | 0.085s | 6.977s |

### 3.2 Per-Class Breakdown

**Baseline (fresh):**

| Class | Count | % | TTFT p50 | TTFT p90 | TPOT p90 |
|-------|-------|---|----------|----------|----------|
| WARM (<5k) | 46 | 23% | 0.137s | 0.262s | 0.061s |
| MEDIUM (5-20k) | 50 | 25% | 0.921s | 1.846s | 0.079s |
| HEAVY (20-50k) | 64 | 32% | 2.660s | 6.278s | 0.076s |
| HEAVY (>50k) | 38 | 19% | 9.587s | 30.415s | 0.102s |

**Elastic P2P (fresh):**

| Class | Count | % | TTFT p50 | TTFT p90 | TPOT p90 |
|-------|-------|---|----------|----------|----------|
| WARM (<5k) | 46 | 23% | 0.142s | 0.279s | 0.072s |
| MEDIUM (5-20k) | 50 | 25% | 0.766s | 1.814s | 0.197s |
| HEAVY (>20k) | 99 | 51% | 6.390s | 22.668s | 0.085s |

### 3.3 Success Rate

| Config | OK | Total | Rate | Failure mode |
|--------|-----|-------|------|-------------|
| Baseline | 198 | 200 | 99.0% | RemoteProtocolError (replayer-side) |
| Elastic P2P | 195 | 200 | 97.5% | 2× RemoteProtocolError + 3× ReadTimeout on >60k |

Elastic's 3 extra errors are D-side KV pull failures: prefill succeeded on P, KV pushed to Mooncake, but D never produced first token (decode scheduler couldn't allocate KV cache space). Prefill timeout fallback (120s → co-located) was never triggered.

### 3.4 Routing Policy: Linear vs LMetric (OSDI'26)

LMetric (`score = P_tokens × BS`, pure per-request, no session affinity) vs Linear (`score = ongoing_tokens - α·cache_hit`, session-sticky). Both fresh-restart, same trace.

> **Errata (2026-05-23)**: Prior LMetric implementation incorrectly shared session-sticky logic with Linear. Fixed to pure per-request routing per OSDI'26 spec: `score = (pending_prefill + new_tokens) × num_requests`, no affinity, no overload override. Results below use corrected implementation.

| Policy | TTFT p50 | TTFT p90 | TPOT p90 | E2E p50 | Delta E2E |
|--------|----------|----------|----------|---------|-----------|
| Linear (session-sticky) | 1.073s | 9.347s | 0.073s | 5.119s | — |
| LMetric (no affinity) | 1.081s | 9.408s | 0.072s | 5.102s | **-0.3%** |

**Key finding**: LMetric without explicit session affinity matches Linear with session affinity on all metrics (< 2% difference). The cache-hit term in LMetric's scoring (`new_tokens = input - cache_hit`) creates **implicit soft affinity** — instances that already cached a session's prefix get lower P_tokens, naturally attracting subsequent turns. Explicit session-sticky routing is not required; cache-aware load balancing captures it automatically.

APC distribution (LMetric, no affinity): inst_0=60.6%, inst_1=58.3%, inst_2=43.2%, inst_3=28.9%, inst_4=16.6%, inst_5=24.0%, inst_6=13.9%, inst_7=0.0%. Non-uniform but comparable aggregate to Linear's explicit affinity.

### 3.5 Errata: Why Prior Cross-Machine A/B Was Invalid

The initial comparison (commit `1e86285`) reported:
```
Baseline (dash0): TTFT50=2.383  E2E50=10.232  ← WRONG (warm instances)
Elastic  (dash1): TTFT50=1.315  E2E50=5.708
Delta:                   -45%          -44%    ← INVALID
```

**Evidence that prior baseline was not fresh:**
1. `inst_7` APC = 68.3% — impossible from 25 cold-start requests (max ~25%)
2. WARM TTFT p90 = 3.327s (fresh = 0.262s, 12.7× gap) — indicates KV cache memory pressure from prior experiments
3. HEAVY TPOT p90 = 0.154s (fresh = 0.076s, 2.0× gap) — heavy prefill-decode interference from full KV cache

The elastic numbers on dash1 were genuinely fresh. The "improvement" was actually comparing fresh elastic against degraded baseline.

### 3.6 Production-Realistic Baseline (trace-driven, corrected methodology)

> Corrected sampling (window+thin, 70% multi-turn, block sharing 48%) and trace-driven replay (no session limit, no time compression). See §2.3 for trace details.

**Linear policy, 912 requests (dense segment), peak QPS ≈ 1.6:**

| Metric | Legacy (§3.1, 1 req/GPU) | **New (trace-driven)** | Delta |
|--------|-------------------------|----------------------|-------|
| TTFT mean | 1.07s | **4.54s** | +4.2× |
| TTFT p50 | 1.08s | **0.94s** | -13% |
| TTFT p90 | 9.38s | **14.12s** | +51% |
| TPOT p50 | 0.038s | **0.070s** | **+84%** |
| TPOT p90 | 0.073s | **0.175s** | **+139%** |
| APC (mean) | ~44% | **67.5%** | **+23pp** |
| Errors | 2/200 (1.0%) | 0/912 (0%) | better |
| E2E p50 | 5.08s | 6.98s | +37% |

**Key differences from legacy methodology:**

1. **APC 67.5% vs 44%**: Window+thin sampling preserves cross-session block sharing (48% vs 16% in legacy random sampling), yielding production-realistic cache hit rates. Per-instance APC ranges 46–84%.

2. **TPOT +139% at p90**: With trace-driven replay, multiple concurrent requests per GPU create **real prefill-decode interference**. The legacy 1 req/GPU setup showed TPOT p90=0.073s (no interference), but production-realistic load shows TPOT p90=0.175s. This validates that prefill-decode interference is a real problem at production concurrency.

3. **TTFT p50 improved (-13%) but mean degraded (+4.2×)**: Higher APC means cached requests get very fast TTFT (p50=0.94s). But concurrent heavy prefills cause queuing for non-cached requests, inflating the mean and p90.

4. **Per-instance APC imbalance (46–84%)**: Routing quality directly determines per-instance APC. The 38pp gap between worst and best instance suggests routing optimization is still the highest-leverage improvement.

**Output**: `outputs/baseline_r0015_st30/` on dash0.

### 3.7 Elastic PS vs Baseline (production-realistic trace)

850 requests, `w600_r0.0015_st30.jsonl`, peak QPS≈1.6. Baseline on dash0, elastic on dash1.

| Metric | Baseline | Elastic PS | Delta |
|--------|----------|-----------|-------|
| TTFT mean | 4.35s | 4.01s | -7.8% |
| TTFT p50 | 0.94s | 0.93s | -1% |
| TPOT p50 | 0.070 | 0.071 | +2% |
| TPOT p90 | 0.162 | 0.157 | -3.1% |
| E2E p50 | 6.38s | 6.44s | +0.9% |
| APC mean | 60.7% | 59.9% | -0.8pp |
| Errors | 0/850 | 4/832 | 4 ReadTimeout |

**Elastic PS is near-neutral.** Root cause analysis:

**Problem 1: Offload gate too restrictive** — only 17/118 HEAVY requests (14%) were offloaded. 75% of HEAVY requests had `cache_ratio=0%` (cold Turn 1), failing the `cache_ratio >= 0.3` gate. The gate was designed to avoid offloading cold requests (full prefill on P is slower than co-located), but this means 86% of HEAVY prefills still interfere with decode.

**Problem 2: Offloaded requests are slower (+50.6%)** — HEAVY_OFFLOAD TTFT=19.94s vs HEAVY_COLO=13.25s. Breakdown:
- Prefill on P: 14.72s (P also queued, no faster than co-located)
- KV transfer + decode start on D: 5.71s (pure overhead)

**Interference is real but unaddressed**: 89% of WARM/MEDIUM requests ran concurrently with 1+ HEAVY prefills (up to 60 concurrent). Elastic PS only offloaded 17/118 HEAVY requests — insufficient to reduce interference.

**Conclusion**: The offload gate (`cache_ratio >= 0.3`) is correct in principle (cold offload IS slower), but leaves the core problem unsolved. Reducing prefill-decode interference requires either:
1. Offloading ALL heavy prefills (accepting higher TTFT for offloaded requests in exchange for lower TPOT for all)
2. Chunked prefill scheduling that yields to decode (vLLM-side optimization)
3. Dedicated prefill GPUs (full PD separation) if KV memory wall can be solved

**Output**: `outputs/eval_baseline_linear/` on dash0, `outputs/eval_elastic_linear/` on dash1.

## 4. System-Level Analysis

### 4.1 Elastic P2P Does Not Improve Single-Machine Performance

Under fair comparison (same machine, both fresh):

| Metric | Baseline | Elastic | Delta |
|--------|----------|---------|-------|
| TTFT p50 | 1.075s | 1.018s | -5.3% |
| TTFT p90 | 9.384s | 11.312s | +20.5% |
| TPOT p90 | 0.076s | 0.085s | +11.6% |
| E2E p50 | 5.075s | 6.977s | +37.5% |

Elastic is **worse** on all metrics except TTFT p50. Root causes:

**1. Mooncake kv_both memory overhead**

Each instance with `kv_role=kv_both` maintains RDMA buffers + Mooncake bootstrap server, reducing GPU memory available for KV cache. This affects ALL requests (including WARM/MEDIUM that don't use P2P transfer), causing more cache eviction and higher TPOT.

Evidence: MEDIUM TPOT p90 = 0.197s (elastic) vs 0.079s (baseline) — **2.5× worse** despite MEDIUM requests not using P2P at all.

**2. D-side KV pull failures**

3 HEAVY requests completed prefill on P instance successfully but D-side never produced first token. The KV cache on D was too full to allocate space for the transferred blocks. These became 600s timeouts.

**3. P2P overhead without proportional benefit**

The P2P path adds: prefill queue on P (p50=6.3s) + KV transfer + decode start on D (p50=0.8s). For requests where the D instance isn't under heavy prefill load (which is the case on fresh instances), co-located execution is faster.

### 4.2 When Elastic P2P Could Help

Elastic P2P is designed for the scenario where D-instance decode is disrupted by co-located heavy prefill. On fresh instances with 200 requests, this contention is moderate. The benefit may emerge under:
- Higher sustained load (1000+ concurrent requests)
- Longer experiment duration (KV cache fills up, eviction pressure increases)
- Multi-machine deployment (P on a different node, no memory competition)

## 5. Data & Log Locations

### 5.1 Experiment Outputs (on respective machines)

| Directory | Machine | Config | Notes |
|-----------|---------|--------|-------|
| `outputs/ab_baseline/` | dash0 | Combined 8× TP=1 | ~~Initial A/B~~ (INVALIDATED: warm instances) |
| `outputs/ab_elastic/` | dash0 | Elastic P2P cap=4 | ~~Initial A/B~~ (INVALIDATED) |
| `outputs/baseline_stability_fresh/` | dash0 | Combined 8× fresh | **Canonical baseline** (§3.1) |
| `outputs/elastic_stability_*/` | dash0 | Elastic P2P kv_both fresh | **Canonical elastic** (§3.1) |
| `outputs/ab_linear/` | dash0 | Linear policy, 200 req | §3.4 routing policy comparison |
| `outputs/ab_lmetric/` | dash0 | LMetric policy, 200 req | §3.4 routing policy comparison |
| `outputs/gpu_ab_combined/` | local | Combined 8× TP=1 | Earlier run, has gpu_util.csv |
| `outputs/gpu_ab_pdsep/` | local | PD-Sep 4P+4D | Earlier run, has gpu_util.csv |
| `outputs/exp2_combined_tp1_dp8/` | local | Combined 8× TP=1 | 1000 req, cache-aware |
| `outputs/exp3_pd_sep_tp1_mooncake/` | local | PD-Sep 4P+4D Mooncake | 1000 req |

### 5.2 vLLM Instance Logs

| Path pattern | Machine | Config |
|-------------|---------|--------|
| `/tmp/ab_base_$i.log` | dash0 | Baseline instances 0-7 |
| `/tmp/ab_elastic_$i.log` | dash1 | Elastic instances 0-7 |
| `/tmp/lmetric_ab_inst_$i.log` | dash0 | Linear policy instances 0-7 (§3.6) |
| `/tmp/lmetric_inst_$i.log` | dash0 | LMetric policy instances 0-7 (§3.6) |

Logs contain `Prefix cache hit rate` and `External prefix cache hit rate` lines for APC extraction.

### 5.3 Trace Data

| Path | Machine | Description |
|------|---------|-------------|
| `~/ali-trace/trace-glm5.1-formatted/051315-051317.jsonl` | dash0 | Full 2h production trace (2.1M requests) |
| `traces/sampled_1000req_seed42.jsonl` | all | Sampled 1000 requests (gitignored, regenerate with `sample_trace.py`) |

### 5.4 Analysis Documents

| File | Content |
|------|---------|
| `analysis/pd_separation_analysis.md` | Main report: PD-Sep vs Combined + Elastic P2P (§5) |
| `analysis/elastic_offload_design.md` | Elastic P2P design rationale |
| `analysis/kv_lifecycle_design.md` | KV cache eviction policy analysis |
| `analysis/adaptive_prefill_offload_design.md` | Initial adaptive offload design (superseded by elastic) |

## 6. Repository Structure

```
agentic-kv/
├── analysis/                    # Research reports and design docs
│   ├── pd_separation_analysis.md    # Main comprehensive report
│   ├── elastic_offload_design.md    # Elastic P2P design
│   ├── kv_lifecycle_design.md       # Cache eviction analysis
│   └── ...
├── replayer/                    # Trace replay framework
│   ├── __main__.py              # CLI entry: python -m replayer
│   ├── replay.py                # Async replayer (session-aware, SSE streaming)
│   ├── trace.py                 # TraceRequest dataclass, session/hash_id handling
│   └── metrics.py               # RequestMetrics, crash-safe JSONL sink
├── scripts/
│   ├── cache_aware_proxy.py     # Global scheduler (combined + PD-sep + elastic offload)
│   ├── sample_trace.py          # Cluster-to-machine trace sampler
│   ├── launch_vllm.sh           # Launch combined TP=8
│   ├── launch_pd_mooncake.sh    # Launch PD-Sep with Mooncake
│   ├── launch_elastic_p2p.sh    # Launch elastic P2P (8× kv_both + offload proxy)
│   ├── run_experiments.sh       # Full experiment matrix (combined/PD-sep)
│   ├── run_benchmark.sh         # Single benchmark run
│   ├── gpu_monitor.sh           # GPU utilization sampler (5s CSV)
│   ├── compute_roofline.py      # Prefill/decode roofline analysis
│   ├── analyze_*.py             # Various analysis scripts
│   └── compare_*.py             # Experiment comparison scripts
├── patches/
│   ├── 0001-fix-kv-transfer-abort-race.patch
│   └── README.md
├── third_party/vllm/            # vLLM 0.18.1 source (with patch applied)
├── outputs/                     # Experiment results (gitignored)
├── traces/                      # Sampled traces (gitignored)
├── TODO.md                      # Original research goals
└── REPORT.md                    # This milestone report
```

## 7. Key Scripts Reference

| Script | What it does | Key flags |
|--------|-------------|-----------|
| `scripts/cache_aware_proxy.py` | Global scheduler + elastic offload proxy | `--combined`, `--offload`, `--policy {linear,lmetric}`, `--heavy-threshold`, `--bootstrap-ports` |
| `scripts/run_lmetric_ab.sh` | A/B: linear vs lmetric routing policy | Runs both experiments with fresh restart |
| `scripts/run_elastic_stability_test.sh` | Elastic vs baseline with full isolation | Fresh start/stop per experiment |
| `scripts/bench.sh` | Standard single-experiment harness | `--tag`, `--mode {baseline,elastic}` |
| `scripts/sample_trace.py` | Sample complete sessions from cluster trace | `--target-requests`, `--seed` |
| `python -m replayer` | Replay trace against vLLM endpoint | `--time-scale`, `--max-inflight-sessions`, `--request-limit` |
| `scripts/gpu_monitor.sh` | Sample nvidia-smi to CSV | Pipe to `outputs/<tag>/gpu_util.csv` |
| `scripts/launch_elastic_p2p.sh` | Launch all 8 kv_both instances + offload proxy | `HEAVY_THRESHOLD`, `MAX_OFFLOAD` env vars |

## 8. GPU Load Imbalance & Elastic Prefill Service Analysis

### 8.1 Load Imbalance Characterization

Session-sticky routing creates token load imbalance across instances. The severity depends on scale:

| Scale | Imbalance | Top 5 sessions | Cause |
|-------|-----------|----------------|-------|
| 200 req (143 sessions) | **8.6×** tokens | 49% of all tokens | Small sample, few dominant sessions |
| 1000 req (668 sessions) | **1.24×** tokens | 29% of all tokens | More sessions → natural averaging |

At 1000 requests, the heaviest instance has 4.5M tokens vs lightest 3.6M (1.24×). Despite this, TPOT is uniform across all instances (0.070–0.077), confirming that prefill-decode interference is minimal at ≤1 session/GPU. The imbalance manifests in **TTFT only**: heaviest 2 instances TTFT p50 = 1.42s vs lightest 2 at 0.83s (1.7× gap).

### 8.2 Session Accumulation Pattern

Agentic workloads produce long-lived sessions with growing context:

| Session | Turns | Total Tokens | Context Growth |
|---------|-------|-------------|----------------|
| 1569319 | 36 | 2.32M | 27k → 98k (+2.0k/turn) |
| 1206593 | 36 | 2.31M | 15k → 106k (+2.6k/turn) |
| 178176 | 25 | 1.93M | 36k → 95k (+2.5k/turn) |

Top 5 sessions = 29% of all tokens. With session-sticky, these lock their instances, creating persistent load hotspots.

### 8.3 Benchmark Concurrency vs Production Reality

> **Critical caveat**: All prior experiments used `--max-inflight-sessions 8` (1 session/GPU). This is **10–15× below production concurrency** and masks the interference that elastic PS is designed to solve.

| | Our Benchmark | Production Estimate |
|--|---------------|---------------------|
| Concurrent requests/GPU | 1–2 | **8–15** |
| KV cache usage/GPU | 26–28% (single req) | **80–100%** |
| Prefill-decode interference | Minimal | **Significant** |

**KV cache capacity**: 281,888 tokens/GPU (25.8 GiB). A single 75k-token request consumes 27% of KV cache. At production concurrency (~15 req/GPU), KV cache is near-full, triggering eviction, cache misses, and prefill queuing — none of which appear in our 1-req/GPU benchmark.

**Measured interference scaling**:

| Concurrency | TPOT p90 | vs 8-session |
|-------------|----------|-------------|
| 8 sessions (1/GPU) | 0.075s | baseline |
| 16 sessions (2/GPU) | 0.103s | **+38%** |
| Production (~15/GPU) | *not tested* | *expected >>+45%* |

### 8.4 Elastic PS: Two Capabilities Re-Evaluated

**Capability 1: Reduce prefill-decode interference (lower TPOT)**

At 1 req/GPU (our benchmark): no interference, no benefit. But this is an artifact of unrealistically low concurrency. At ≥2 req/GPU, chunked prefill interrupts decode steps, causing TPOT +38–45%. At production concurrency (~15/GPU), multiple HEAVY prefills sharing a GPU with decode requests would cause severe interference. Elastic PS's ability to isolate heavy prefills on separate GPUs directly addresses this.

**Capability 2: Session migration for load balancing**

Elastic PS enables mid-session migration: prefill on original instance (cache hit), KV transfer to a different instance for decode + future turns. This provides two benefits:

1. **Break session lock-in**: Agentic sessions grow +2k tokens/turn over 30+ turns. With session-sticky, a 36-turn session (2.3M tokens total) locks its GPU, creating a hotspot. Elastic PS lets the session migrate to a less-loaded GPU while preserving cache on the original (PS does fast cached prefill, new GPU decodes).

2. **Rebalance without cache loss**: Unlike breaking affinity (which destroys cache), elastic PS migration preserves the prefix cache on the original instance — the PS re-uses it for fast prefill, then transfers only the new KV to the destination.

Simulation of migration strategies (1000 req, at current low concurrency):

| Strategy | Imbalance | Migrations | KV Transfer Overhead |
|----------|-----------|------------|---------------------|
| No migration | 1.24× | 0 | 0s |
| Every 10 turns | 1.21× | 10 | 15s |
| Every 5 turns | 1.20× | 20 | 30s |

At 1 req/GPU, migration benefit is marginal (imbalance is only 1.24×). At production concurrency where imbalance combines with KV cache pressure and interference, the benefit would be substantially larger.

**Capability 3: Soft affinity from cache-hit scoring**

The corrected LMetric experiment (§3.4) reveals that **explicit session affinity is unnecessary**. Cache-hit scoring (`new_tokens = input − cached`) creates implicit soft affinity — instances with cached prefixes score lower, naturally attracting subsequent turns. This matches hard session-sticky on all metrics (< 2% difference) while providing more flexible load balancing.

### 8.5 Elastic PS Verdict

| Aspect | At 1 req/GPU (tested) | At production load (expected) |
|--------|----------------------|-------------------------------|
| TPOT reduction | 0% (no interference) | **Significant** (interference scales with concurrency) |
| Session migration | Marginal (1.24× → 1.20×) | **Larger benefit** (KV pressure + interference amplify imbalance) |
| Cache preservation | N/A | **Key advantage** vs breaking affinity |

**At our benchmark concurrency (1 req/GPU), elastic PS is not justified** — Mooncake overhead exceeds the non-existent interference benefit. **But our benchmark is 10–15× below production load.** The real question is whether elastic PS helps at production-realistic concurrency (64–128 concurrent sessions, 8–15 req/GPU), where:
- Prefill-decode interference is measurable (already +38% TPOT at just 2/GPU)
- KV cache pressure causes eviction and queue delays
- Session accumulation creates compounding hotspots
- Heavy prefills (50–100k tokens) block decode for seconds

**Next step: run `--max-inflight-sessions 64` benchmark** to test elastic PS at production-realistic concurrency.

## 9. Conclusions & Next Steps

### Established findings:
1. Full PD separation is **net negative** for single-machine agentic workloads (KV cache memory wall)
2. Cache-aware routing is the **dominant optimization** (+24pp APC, -60% TTFT vs round-robin)
3. **Explicit session affinity is unnecessary** — cache-hit scoring creates implicit soft affinity that matches hard session-sticky (< 2% difference)
4. At low concurrency (1 req/GPU), elastic P2P offload adds overhead without benefit
5. **Our benchmark concurrency is 10–15× below production**: `--max-inflight-sessions 8` yields 1 req/GPU, masking prefill-decode interference that appears at ≥2 req/GPU (+38% TPOT) and would dominate at production load (~15 req/GPU)
6. **Experimental methodology matters**: warm vs fresh instances cause 2× TTFT difference

### Lessons learned:
- Prior cross-machine A/B (commit `1e86285`) was invalid — warm baseline inflated by 2×
- Prior LMetric implementation was invalid — incorrectly shared session-sticky logic with Linear
- `kv_role=kv_both` has non-trivial always-on overhead even when P2P transfer is not used
- Experiment isolation (kill all → verify GPU free → fresh start) is critical for reproducibility
- **Benchmark concurrency must match production** — sub-production concurrency hides interference effects that drive system design decisions

### Open problems (priority ordered):
1. **Production-concurrency benchmark** (`--max-inflight-sessions 64+`): Validate whether prefill-decode interference at 8–15 req/GPU makes elastic PS net-positive
2. **Multi-machine elastic**: P on a different node eliminates GPU memory competition — the main cost that makes single-machine elastic net negative
3. **Layerwise KV transfer**: Mooncake's block-level transfer after full prefill is the bottleneck. Layerwise pipelining could reduce transfer latency by overlapping with computation
4. **Router state accuracy**: proxy shadow state vs vLLM-internal exact state (TODO: vLLM → Redis → router)

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*Updated 2026-05-23. LMetric corrected (§3.4 errata). GPU imbalance analysis added (§8). Benchmark concurrency gap identified — production-load experiments needed.*
