Gahow Wang
da39ab6804
The §3.2 cost-vs-benefit math in commits029821c(MB1 plot + pd_cost_vs_benefit.png) andabde010(RESULTS_SUMMARY.md) was wrong. What was wrong: I framed PD-disagg's max phase-isolation benefit as "≤ decode duration of the new request (~50–200 ms)" — implicitly treating the benefit as per-request and bounded by that request's own decode. The correct accounting is per-prefill-event across all stalled streams: benefit_per_prefill = D × T_prefill × (1 − TPOT_baseline/TPOT_during) ≈ D × T_prefill which follows from the chunked-prefill math (each of L/N chunks slows D ongoing decode steps from ~10 ms to t ms, summing to D × T_prefill). Plug MB1 + MB2 numbers in: prefill size | T_prefill | T_transfer | D=8 benefit | cost/benefit 2k tok | 0.14 s | 8 ms | 1.1 s | 0.7 % 33k tok | 4.5 s | 320 ms | 36 s | 0.9 % 125k tok | 57 s | 1.9 s | 456 s | 0.4 % On the phase-isolation axis alone, PD-disagg WINS by 100×–250× — the opposite of what the deleted figure showed. The actual dominant reason static PD-disagg fails in agentic is the D-side KV pool capacity wall (figs/f4b_pdsep_kv_wall.png) — p99 single-request KV is 11.5 GiB, per-D-instance pool is 38 GiB, so 4P+4D halves system decode capacity. Colleague's 4P+4D experiment showed TTFT p50 62× worse and success rate 99.5% → 52%, driven by pool overflow + queueing, not by transfer latency. Changes (all touched files explicitly listed; no `git add -u`): - figs/pd_cost_vs_benefit.png : DELETED (figure built on wrong math) - microbench/fresh_setup/plot_mb1.py : drop the pd_cost_vs_benefit function; keep mb1_interference.png and update its title to note per-prefill aggregate stall = D × T_prefill (not capped by decode) - figs/mb1_interference.png : regenerated, no misleading band annotation - analysis/mb1/README.md : Summary block rewritten ("what MB1 measures"; no more "max benefit = decode duration" claim); §3.2 implications section replaced with the corrected per-prefill-event table; explicit ⚠ Correction note documents what was wrong - analysis/mb2/README.md : Summary block + §3.2 implications section rewritten the same way; ⚠ Correction note links to RESULTS_SUMMARY §4 - RESULTS_SUMMARY.md §4 + §6 : §4 reordered to lead with the D-side capacity argument (the real failure mode), MB1/MB2 demoted from "kill-shot for PD-disagg" to "supporting context inputs to a cost-benefit table that actually favors PD-disagg on this axis"; §6 paper-claims list reordered to remove the wrong "PD-disagg loses on cost-vs-benefit" claim and replace with the corrected ones PAPER_OUTLINE.md and MEETING.md were checked and never picked up this specific wrong claim — they already (correctly) frame §3.2 around the D-side KV memory wall. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
123 lines
8.2 KiB
Markdown
123 lines
8.2 KiB
Markdown
# 目前已成立的结论（2026-05-27）
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EAR 项目目前能用实测数据支撑的论点汇总。每条都标了对应的图/数据路径。
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---
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## 1. Workload 性质（§2）
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Production trace = Qwen3-Coder agentic，1.3 M sessions / 2.1 M reqs / 7200 s。
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| 性质 | 数据 | 实证 |
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|---|---|---|
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| **KV 复用几乎全在 session 内** | intra 93.2% / cross 5.7% / shared 1.1%；理论 APC 上界 79.6% | `figs/f2a_reuse_topology.png` |
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| **Session 极度偏斜** | top 1%/5%/10%/25%/50% = 46.5%/66.5%/74.6%/87.5%/**96.0%** input mass | `figs/f2b_session_skew.png` |
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| **单请求 KV 已经很大** | p50 1.8 GiB / p90 8.0 / p95 9.6 / **p99 11.5 GiB**；KV pool 38 GiB/instance（0.4 × H20 96 GiB）→ p99 req 只能装 **3 个/instance** | `figs/f2c_kv_footprint_cdf.png` |
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**结论**：cache 是 session-local 的，scheduling 必须保留 session affinity；单 request KV 接近 pool 上限，**PD-disagg 4P+4D 让系统 decode 容量直接减半**。
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## 2. Dispatch Coupling（§2.3）
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| 数据 | Agentic (Qwen3-Coder) | Chatbot (qwen3-max) |
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|---|---:|---:|
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| Inter-turn `T_external` p50 | **1.6 s** | 7.2 s |
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| `gap < 1 s` 比例 | **39%** | 4% |
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| `gap < 5 s` 比例 | 67% | 29% |
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| p99 | 738 s | 43 s |
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参考图：`figs/f3a_inter_turn_gap.png`。
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**结论**：agentic 有一段 chatbot 没有的 **sub-second tool-call mode**（39% vs 4%）。当 `W_turn ≳ T_external`（任何 W_turn > 1 s 的 scheduler 在 agentic 上都满足这条件），Little's Law `L = Λ · N · (W_turn(L) + T_external)` 进入闭环 regime，scheduler 的 ε 退步通过 KV 竞争反馈环被放大成 wall-clock 数倍差距。**实测**：lmetric 跑 600 s trace 用 49 min wall-clock = **8x amplification**。
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## 3. 现有调度的三类失败（§3）
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| Baseline | 失败模式 | 数据 |
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|---|---|---|
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| **load-balance / LMetric** | 丢 locality | lmetric APC **56.9%**（vs 上界 79.6%）；LMetric 比 load_only 只好 +3.3pp，因为 cache 信号在乘性 score `(pending+input−hit) × num_req` 里被 num_req 吞掉 |
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| **静态 PD-disagg** | D 侧 KV 容量墙 + transfer 成本 | 见 §4 cost-vs-benefit |
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| **Pure sticky** | 全员被 hot session 拖累，不是单一热点 | sticky median worker 20.3 s vs unified 10.3 s；system e2e p90 sticky 34.6 s vs unified 18.0 s（**用 max/median ratio 衡量是误导**，§3.3 用 absolute per-worker latency）|
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参考图：`figs/f4a_apc_loss.png`、`figs/f4b_pdsep_kv_wall.png`、`figs/f4c_per_worker_ttft.png`、`figs/f6_e2e_latency_bars.png`、`figs/f6_e2e_latency_full_grid.png`。
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## 4. Static PD-disagg 为什么失败（§3.2）—— 容量问题，不是 cost-benefit 问题
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⚠ **2026-05-27 纠正**：本节前一版本论证"PD-disagg 因为 transfer cost > phase isolation benefit 而失败"。**这个论证算错了**。正确的 phase-isolation benefit 是**每个 prefill 事件 × D 个 concurrent stream** 的总和（≈ `D × T_prefill`），不是单个 request 的 decode 时长。用正确公式，PD-disagg 在 phase-isolation 这一维上**赢 colo 一两个数量级**。Static PD-disagg 在 agentic 上失败的**真正根因是 D 侧 KV pool 容量**，不是这一维。
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### 4.1 真正的失败模式：D 侧 KV 容量天花板
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| | 8C colo | 4P+4D PD-disagg |
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|---|---:|---:|
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| Per-D-instance KV pool（0.4 × 96 GiB） | 38 GiB | 38 GiB |
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| 系统总 decode 容量（D 实例数 × 单池） | 8 × 38 = **304 GiB** | 4 × 38 = **152 GiB** |
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| p99 单请求 KV = 11.5 GiB → 最多并发 decode | 24 | **12（减半）** |
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Colleague 4P+4D 实测：TTFT p50 0.91 s → **62.8 s（62×）**、success rate **99.5% → 52%**。失败模式：**D 池溢出 + 排队**，不是 transfer 延迟。
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参考图：`figs/f4b_pdsep_kv_wall.png`（pdf 版本是高质量 paper figure）。
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### 4.2 MB2 — KV transfer cost（per-request 一次性成本，**不**是 dominant cost）
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dash1 GPU 0+1（intra）和 dash1 ↔ dash2（inter, 200 Gbps RoCE）扫 9 个 size × 5 reps。
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| 路径 | 稳态带宽（≤ 3 GiB） | p99 agentic 请求 11.5 GiB transfer |
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|---|---|---|
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| Intra-node | **9.7 GB/s** | p50 **1.9 s** · max 10 s |
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| Inter-node | **10.0 GB/s**（差 <3%） | p50 **1.7 s** · max 9.2 s |
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**新发现**：intra/inter 几乎重合 → **Mooncake `batch_transfer_sync_write` 永远走 RDMA NIC**，不走 NVLink。200 Gbps NIC 是天花板。**PD-disagg transfer cost 与拓扑无关**。
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参考图：`figs/mb2_transfer_time_compare.png`、doc `analysis/mb2/README.md`。
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### 4.3 MB1 — Phase interference（PD-disagg 的潜在 benefit 上界）
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dash1 GPU 0 单 instance（无 kv_connector），chunked-prefill 默认开启，D × P 扫描。D=8 结果：
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| Prefill | T_prefill | per-stream TPOT during | penalty |
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|---|---:|---:|---:|
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| 2k tok | 143 ms | 32 ms | 4× |
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| 32k tok | 4.5 s | 388 ms | **52×** |
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| 131k tok | 57 s | 1419 ms | **183×** |
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**Decode 在 prefill 期间被几乎完全 halted**，单 stream 损失 ≈ `T_prefill` 的时间。**每个 prefill event 总 decode 损失 ≈ `D × T_prefill`**。
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参考图：`figs/mb1_interference.png`、doc `analysis/mb1/README.md`。
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### 4.4 联合 cost-benefit（per-prefill event）
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| Prefill (KV size) | T_prefill | Cost = T_transfer | Benefit = D × T_prefill (D=8) | Cost / Benefit |
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|---:|---:|---:|---:|---:|
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| 2k tok (192 MiB) | 0.14 s | 8 ms | 1.1 s | **0.7%** |
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| 33k tok (3 GiB, trace mean) | 4.5 s | 0.32 s | 36 s | **0.9%** |
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| 125k tok (12 GiB, ~p99) | 57 s | 1.9 s | 456 s | **0.4%** |
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**PD-disagg 在 phase-isolation 这一维赢 100×–250×**。但**这不是 §3.2 该用的论证**，因为 §3.2 真正的 dominant failure 是 §4.1 的 D 池容量天花板（颠覆了上面的全部数学）。
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**总结**：
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- D 侧 KV 容量天花板（§4.1）→ PD-disagg 在 agentic 上**结构性失败**。
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- MB1 + MB2 的合计 cost-benefit 在 phase isolation 维度上 PD-disagg 是赢的，**但这件事被容量天花板压倒**。
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- Paper §3.2 论证应该聚焦"D 池装不下"，MB1/MB2 数据用作 supporting context（per-request transfer charge 量化、phase isolation benefit 量化）而不是 main argument。
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## 5. EAR 设计的实证状态（§4）
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| Pillar | 已实证 | 待实证 |
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| **Affinity-default routing** (Pillar 1) | ✅ Current `unified` 算法 = LMetric + high-cache affinity；APC **79.4%**（达到 79.6% 上界 97%），TTFT p90 **7.3 s**，median worker p90 **10.3 s** | — |
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| **Hot-triggered session migration** (Pillar 2) | substrate 已通：`kv_both` connector 在 trace replay 上 net positive（TTFT p90 −18.6%，DR-fix 后 −36.6%），原 elastic_migration_v2 paper 的 "+45% kv_both penalty" obsolete | e2e 策略层（trigger 阈值 + target selection 在反馈环里）未直接验证 |
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## 6. 已经能写的 paper 主张（按 confidence 排序）
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1. **Agentic vs chatbot 在调度上是不同 regime**（dispatch coupling + sub-second tool-call mass）—— 实证完整
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2. **三类现有调度 baseline 各自的失败模式**（load-balance / static PD-disagg / pure sticky）—— 实证完整
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3. **Static PD-disagg 在 agentic 下失败的 dominant 根因是 D 侧 KV 容量**（不是 phase-isolation cost-benefit）—— 实证完整（`f4b` + colleague 4P+4D 数据）
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4. **Mooncake transfer cost 拓扑无关**（intra ≈ inter，~9.7 GB/s NIC 上限）—— 实证完整（MB2）
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5. **Phase isolation interference 在 chunked-prefill on 下仍然显著**（per-stream TPOT during prefill 15×–2000× baseline）—— 实证完整（MB1）。**注意**：这条数据本身不直接论证 "PD-disagg 失败"，因为算正确账后 PD-disagg 反而在这一维上赢；它的用途是给 §3.2 提供 phase-isolation benefit 上界的量化。
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6. **Affinity-default 调度（current unified）达到 APC 上界**，per-worker latency 也压倒 sticky —— 实证完整
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7. **Hot-triggered migration 修复 sticky 的 hot pin** —— design 完整、e2e 待验证
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## 7. 待做
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- **MB3-5**（end-to-end PD-disagg deployment）：D-pool runtime occupancy、cache reuse × PD interaction、PD ratio sweep。这些是 §5 完整实验矩阵的事
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- **EAR Pillar 2 migration e2e validation**（在 connector_tax DR-fix 之上重测）
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- **§5.4 wall-clock amplification sweep**（5 baseline × 3 runs，钉死 dispatch coupling 论证的实证 closure）
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- **Scale-out 验证**（dash1+dash2 = 16 GPU，等 dash0 + 3-node 可用时扩到 80 GPU）
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