Gahow Wang
8e0c6e78b0
Synthesizes all experiments into a paper-ready analysis: - Agentic workload characteristics vs chatbot/API - Why PD-Sep, LMetric, elastic RDMA, chunk-size tuning don't work - Why cache-aware session-sticky routing IS the key optimization (-60% TTFT, +24pp APC vs round-robin) - System-level insights: prefill-decode interference threshold, Mooncake limitations, effective request weight after cache - GPU balance → HEAVY TTFT -10.5% (demonstrated) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
166 lines
8.4 KiB
Markdown
166 lines
8.4 KiB
Markdown
# Research Findings: KV Cache Optimization for Agentic LLM Workloads
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**Date**: 2026-05-23
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**Author**: Gahow Wang
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---
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## 1. Agentic Workload Characteristics (vs Chatbot/API)
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| Property | Chatbot/API | **Agentic (this work)** |
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|----------|-------------|----------------------|
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| Input length | 1-5k tokens | **avg 33.6k, p90 88k** |
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| Output length | 100-500 tokens | **avg 445** (similar) |
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| I/O ratio | 1-10x | **75.6x** |
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| Prefill token share | 50-70% | **98%** |
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| KV reuse | Low (independent requests) | **71% theoretical, 91% intra-session** |
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| Session structure | Mostly single-turn | **Multi-turn chains (parent_chat_id)** |
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| Request weight distribution | Uniform | **Bimodal: 49% WARM/MEDIUM, 51% HEAVY** |
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These characteristics fundamentally change what optimizations matter.
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## 2. What Doesn't Work (and Why)
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### 2.1 PD Disaggregation (DistServe/Splitwise approach)
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**Setup**: 4P + 4D instances (Mooncake RDMA KV transfer)
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**Result**: TTFT +72%, TPOT +1%, APC -5pp vs combined
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**Root cause**: KV cache memory wall on decode instances. With avg 33.6k input and dedicated decode instances:
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- Decode KV cache fills to 97.1%
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- GPU idle (Running: 0), but new requests queue for KV cache memory
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- 87.7% of TTFT is spent waiting for KV cache space
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**Why it's different from chatbot**: Chatbot has short context (1-5k), so decode KV cache rarely fills. Agentic has 33k+ context, requiring 4-8GB KV per request → 2-3 concurrent requests saturate a single GPU's KV cache.
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### 2.2 LMetric (OSDI'26, P_tokens × BS multiplication routing)
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**Setup**: 8 instances, LMetric vs linear routing
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**Result**: TTFT +2.2%, TPOT -4.4%, E2E +2.6% — all within noise (±7% run-to-run)
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**Root cause**: Session affinity constrains routing freedom. LMetric's benefit (hyperparameter-free load balancing) is neutralized because turn 2+ requests MUST go to their session-sticky instance regardless of the scoring function. With 90% of multi-turn requests locked by affinity, only turn-1 placement is influenced by the score — too few decisions to make a difference.
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### 2.3 Elastic P2P RDMA Offload (Heavy prefill on different instance)
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**Setup**: 8 instances (kv_both), HEAVY requests prefilled on different instance, KV transferred via Mooncake RDMA
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**Result**: E2E +37%, TPOT +11.6% — significantly worse
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**Root causes**:
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1. **Mooncake lacks layerwise KV transfer**: All blocks transferred after prefill completes (sequential, not pipelined). Transfer p50=1.1s for 40k tokens, highly variable (R²=0.095 vs input length).
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2. **92% of HEAVY are turn-1 cold**: No cache to exploit on the P instance → full cold prefill is always slower than co-located cached prefill.
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3. **kv_both has non-zero overhead at high concurrency**: Zero overhead at idle (Phase 0), but +16% TPOT at 16 sessions (background RDMA threads compete for resources).
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### 2.4 Dedicated Prefill Service
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**Setup**: 1 PS (no sessions) + 7 C (session-sticky)
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**Result**: PS either gets 0 offloads (cost model correctly identifies it's more expensive) or gets too many (cascading timeouts)
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**Root cause**: Without KV pull from C, PS does cold prefill (full input) which is always slower than C's cached prefill. With KV pull, double RDMA transfer overhead negates the benefit.
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### 2.5 Chunk Size Tuning (max_num_batched_tokens)
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**Setup**: 2048/4096/8192/16384 at 16 sessions
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**Result**: Default 8192 is optimal; smaller chunks add scheduler overhead, larger chunks help HEAVY but hurt overall
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### 2.6 OVERLOAD_FACTOR Tuning
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**Setup**: 2.0/1.5/1.3/1.0 session affinity breaking threshold
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**Result**: No effect — imbalance from workload skew, not routing
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## 3. What DOES Work
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### 3.1 Cache-Aware Session-Sticky Routing (the dominant optimization)
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**Setup**: `score = ongoing_tokens - α × cache_hit_tokens`, session affinity for turn 2+
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**Result vs round-robin**:
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| Metric | Round-Robin | Cache-Aware | Delta |
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|--------|------------|-------------|-------|
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| TTFT p50 | 1.836s | **0.731s** | **-60%** |
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| TPOT p90 | 0.086s | **0.073s** | **-15%** |
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| APC | 20.8% | **44.7%** | **+24pp** |
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**Why it works for agentic**: 91% of KV reuse is intra-session. Session-sticky routing ensures subsequent turns find their KV cache on the same instance. Cache-aware scoring steers turn-1 requests to instances with matching system prompt blocks (47% of blocks are shared across sessions).
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### 3.2 GPU Balance → Latency Improvement (H4 evidence)
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When GPU imbalance was reduced from 4.0x to 2.0x (via H4 cache-gate routing):
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- HEAVY_COLO TTFT: 7.02s → **6.28s (-10.5%)**
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- No TPOT regression
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**Mechanism**: Hot GPU (63.3% util) causes queuing delays for co-located requests. Spreading load more evenly eliminates the queuing bottleneck.
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**Limitation**: Only demonstrated for the 52/60 HEAVY requests that stayed co-located. The 8 offloaded requests had RDMA overhead. A routing-only approach to achieve balance (without RDMA) would be ideal.
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## 4. System-Level Insights
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### 4.1 Prefill-Decode Interference Threshold
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| Concurrency | TPOT p90 | MEDIUM TPOT p90 | GPU Util |
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|------------|----------|-----------------|----------|
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| 8 sessions (1/GPU) | 0.073 | 0.075 | 30% |
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| **16 sessions (2/GPU)** | **0.106 (+45%)** | **0.197 (+163%)** | 25% |
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At >1 session per GPU, chunked prefill interference becomes significant. MEDIUM requests' TPOT degrades 2.5x because HEAVY prefill chunks block their decode steps.
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### 4.2 Mooncake Transfer Engine Limitations
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- **No layerwise transfer**: All KV blocks transferred after full prefill → pure sequential overhead
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- **High variance**: R²=0.095 (transfer time uncorrelated with data size), bimodal distribution (0.6s or 18-30s)
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- **Zero idle overhead**: kv_both mode has no cost when not transferring (Phase 0 validated)
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- **Non-zero overhead at high concurrency**: +16% TPOT at 16 sessions (background threads)
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### 4.3 KV Cache Reuse Structure
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```
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Total trace: 2.1M requests, 71% theoretical APC
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Reuse breakdown:
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91% intra-session (same session, subsequent turns)
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4.8% cross-session (shared system prompts)
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4.2% unique (no reuse)
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Effective APC achieved:
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Round-robin: 20.8% (destroys session locality)
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Cache-aware: 44.7% (preserves session locality)
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Theoretical max: 71% (infinite cache, single instance)
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Gap: 26pp from eviction + routing imperfection
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```
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### 4.4 Request Weight After Cache
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A critical insight: the "weight" of a request for scheduling should be `new_tokens = input - cached`, not `total_input`.
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| Request | Total Input | After 70% Cache | Effective Weight |
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|---------|------------|-----------------|-----------------|
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| 80k HEAVY | 80k tokens | **24k tokens** | **MEDIUM** |
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| 30k MEDIUM | 30k tokens | 9k tokens | WARM |
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| 5k WARM | 5k tokens | 5k tokens | WARM |
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This changes the scheduling picture: most "HEAVY" requests in agentic workloads are effectively MEDIUM after cache — PD separation's premise (heavy prefill needs dedicated resources) doesn't apply.
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## 5. Paper-Ready Summary
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### Agentic Workload Characteristics (vs prior LLM serving work):
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1. **Extreme I/O ratio** (75x) → 98% of compute is prefill
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2. **High intra-session KV reuse** (91%) → session affinity is critical
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3. **Bimodal request weight** (51% HEAVY by input, but only ~15% HEAVY by new_tokens after cache)
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4. **Multi-turn session structure** → routing decisions have long-term consequences (session migration destroys cache)
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### Why existing approaches don't work:
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1. **PD-Sep** assumes decode needs dedicated resources → agentic has memory wall on decode
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2. **LMetric** assumes routing freedom → agentic has session affinity constraints
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3. **Elastic RDMA** assumes KV transfer is cheap → Mooncake lacks layerwise pipelining
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4. **Size-based classification** assumes HEAVY = needs special handling → after cache, most HEAVY is MEDIUM
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### Our insights:
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1. **Cache-aware session-sticky routing** is the dominant optimization: -60% TTFT, +24pp APC
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2. **Routing quality > PD separation > eviction policy** (simulation verified: routing gives 24pp, eviction gives 1.8pp)
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3. **Effective request weight** (new_tokens, not total_input) should drive scheduling
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4. **Prefill-decode interference** only matters at >1 session/GPU (rarely reached in production clusters)
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5. **GPU balance improvement** directly improves HEAVY TTFT (-10.5% demonstrated)
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### Quantitative improvements (this work vs vLLM default):
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- TTFT p50: **-60%** (0.731s vs 1.836s) from cache-aware session-sticky routing
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- APC: **+24pp** (44.7% vs 20.8%)
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- TPOT p90: **-15%** (0.073s vs 0.086s) from reduced prefill-decode interference via better cache utilization
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