agentic-kvc/analysis/elastic_hypotheses.md

Elastic Prefill Service: Hypotheses and Validation Log

Date: 2026-05-23 Context: Investigating whether elastic PD disaggregation can improve agentic LLM serving vs pure co-located baseline.

Baseline Reference (8C plain, fresh restart, 200 req)

OK=198/200  TTFT50=1.075  TTFT90=9.384  TPOT90=0.0761  E2E50=5.075
WARM:   TTFT50=0.137  TPOT90=0.061
MEDIUM: TTFT50=0.921  TPOT90=0.079
HEAVY:  TTFT50=4.945  TPOT90=0.076

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H1: Mooncake kv_both has significant runtime overhead

Claim: Enabling kv_both mode degrades TPOT even without KV transfer (RDMA threads, ZMQ sockets compete for CPU).

Prior evidence: Earlier elastic P2P experiment showed MEDIUM TPOT 0.079→0.197 (+150%). Attributed to kv_both overhead.

Experiment: Phase 0A (7C kv_both, no offload) vs Phase 0B (7C plain)

Result: TPOT90 = 0.0738 (kv_both) vs 0.0729 (plain) → +1.3%, within noise

Verdict: REJECTED. kv_both has zero runtime overhead. The earlier 150% TPOT degradation was from offload-induced interference, not kv_both itself.

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H2: Dedicated Prefill Service (PS) without KV pull improves HEAVY TTFT

Claim: A dedicated PS instance (no sessions) does HEAVY prefill without disrupting C's decode. PS does full cold prefill (no cache), D (session-sticky C) pulls KV and decodes.

Experiment: PS V1 — 1PS + 7C kv_both, always offload HEAVY to PS

Result:

• ps_always: OK=195/200, HEAVY TTFT p50=~7.8s (baseline 5.0s, +56%), cascading timeouts
• ps_cost: 0 offloads (cost model correctly identifies PS is more expensive)
• ps_flexd: OK=172/186 (92.5%), HEAVY TTFT p50=7.8s, 12 ReadTimeout

Root cause: PS has no KV cache for the session → full cold prefill is SLOWER than C's cached prefill. Cost model: full_input/8333 > (input-cached)/8333 + interference is always true.

Verdict: REJECTED. PS without KV pull cannot beat cached co-located prefill. The cold prefill overhead + KV transfer time exceeds the interference savings.

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H3: C_s cached prefill + flexible D decode (V2) improves E2E

Claim: C_s (session-sticky, has cache) does fast prefill (max_tokens=1), D (least-loaded C) pulls KV via Mooncake and does decode. Benefits: (1) C_s prefill is fast due to cache, (2) D is least-loaded so decode starts quickly, (3) session migrates to D for better load balance.

Experiment: V2 — 8C kv_both, HEAVY offloaded (C_s prefill → flexible D decode)

Result:

OK=179/185 (96.8%)  TTFT50=0.762 (-29%)  E2E50=4.628 (-9%)  TPOT90=0.0746 (=)
HEAVY: TTFT50=4.794 (≈baseline)  TTFT90=20.4 (+117%)
Routes: 63 HEAVY_OFFLOAD, 51 MEDIUM, 69 WARM
Cache hit on offloaded: mean=3%, median=0% (92% are turn-1 cold)
Prefill: p50=5.0s  D KV pull: p50=1.1s p90=6.7s

Partial validation: E2E p50 improved 9%, TTFT p50 improved 29%. But HEAVY p90 degraded 2x and 6 errors (vs 2 baseline).

Key finding: 92% of HEAVY requests are turn-1 (zero cache on C_s). C_s does COLD prefill anyway → offload adds pure RDMA overhead (~1.1s) with no cache benefit.

Verdict: PARTIALLY VALIDATED. The architecture works for MEDIUM and WARM (better load balance). But blindly offloading all HEAVY hurts because most are cold.

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H4: Only offload HEAVY with high cache hit (cold HEAVY should stay co-located)

Claim: Turn-1 HEAVY requests have zero cache → co-located is faster (no RDMA overhead). Only turn-2+ HEAVY with significant cache hit (>50%) should be offloaded, because:

• C_s's prefill is fast (only new tokens computed)
• D gets the KV via RDMA (~1.1s, small vs the savings from not waiting for C_s's decode queue)
• C_s's decode is not disrupted

Counterintuition: This challenges the conventional PD-sep assumption that "all heavy prefill should be disaggregated." For agentic workloads with high cache reuse (70%+), most of the "heavy" prefix is already cached — the actual compute is MEDIUM-level.

Experiment: TODO — V2 with cache_hit > 50% * input_length gate

Expected:

• Turn-1 cold HEAVY stays co-located (no RDMA overhead, same TTFT as baseline)
• Turn-2+ cached HEAVY gets offloaded (C_s fast prefill + D least-loaded decode)
• Overall: HEAVY TTFT ≈ baseline, HEAVY TPOT improved (D less loaded), fewer errors

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H5: RDMA KV transfer overhead (1.1s p50) is too high — should be pipelined

Claim: The 1.1s p50 KV transfer time for HEAVY requests (~40k tokens) seems excessive. At 200Gbps RDMA (25 GB/s), 40k tokens × 96KB/token = 3.75GB → should take ~0.15s. The 7x gap suggests block-by-block transfer without pipelining.

Questions to investigate:

1. Does Mooncake do layerwise KV transfer? (transfer layer N while computing layer N+1)
2. Is the 1.1s from RDMA setup overhead, block scatter, or actual bandwidth?
3. Does vLLM's chunked prefill interact with the transfer (blocks only available after each chunk)?

From Mooncake code: MooncakeConnector does not do layerwise saving (comment in code). All blocks are saved/loaded after the FULL prefill completes. This means:

• Prefill must complete entirely before ANY KV transfer starts
• D cannot start decode until ALL blocks arrive
• No overlap between prefill compute and KV transfer

Potential optimization: Layerwise transfer would allow D to start pulling layer 0's KV while C_s is still computing layer 47's KV. This could reduce the effective transfer latency to near zero (hidden behind compute).

Experiment: TODO — Profile actual RDMA transfer time vs setup overhead. Check if start_load_kv() and wait_for_layer_load() APIs support layerwise loading (they exist in the interface but Mooncake doesn't implement them).

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H6: Session migration breaks KV cache locality for future turns

Claim: When a HEAVY request is offloaded from C_s to D, session affinity moves to D. But D starts with zero cache for this session — it only has the KV from the current turn (transferred via RDMA). Future turns go to D, which now has the current turn cached. But the RDMA-transferred KV might not be properly registered in D's prefix cache.

Questions:

• Does vLLM's prefix cache recognize RDMA-transferred blocks as cacheable?
• If yes, future turns on D should have similar APC to staying on C_s.
• If no, every turn after migration is a cold start on D.

From vLLM metrics: external_prefix_cache_hits_total counts cross-instance cache hits. If this is > 0 on D after migration, the transferred blocks ARE cacheable.

Experiment: TODO — Track per-instance APC before and after session migration. Check if D's APC for migrated sessions matches expectations.

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Summary of Current Understanding

                    Turn 1 (cold)           Turn 2+ (cached)
                    ─────────────           ────────────────
Co-located:         ✅ Best (no overhead)   ⚠️ HEAVY disrupts decode
Offload (V2):       ❌ Adds RDMA overhead   ✅ C_s fast prefill + D load balance

The optimal strategy is hybrid: co-locate cold turn-1, offload cached turn-2+.

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.