f2b: regenerate CDF from production trace (1.3M sessions on dash0)

Pulls 456 (rank%, cum%) sample points from the raw production trace at
dash0:/home/admin/cpfs/wjh/ali-trace/trace-glm5.1-formatted/051315-051317.jsonl,
cached locally so the figure is reproducible without ssh access. Sampled
anchors match the precomputed summary exactly:
  top 1% = 46.5%, top 5% = 66.5%, top 10% = 74.6%
plus newly readable points:
  top 25% = 87.5%, top 50% = 96.0%

Workload characterization is now consistent with the production
distribution rather than the small replay subset. Replay window CDF kept
as an overlay to show the same hockey-stick shape on the data §5 actually
uses.

- analysis/characterization/data/production_session_skew_cdf.json: cached
  sample points (29 KB), so the figure rebuilds locally
- scripts/plot_session_skew_cdf.py: now plots from the cache + replay raw
- MEETING.md / PAPER_OUTLINE.md: revert numbers to production trace,
  add top-25%/50% data points

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

MEETING.md

@@ -26,7 +26,7 @@ L = Λ · N · W_turn(L)        # agentic, T_human≈0
 | | 数据 | 图 |
 |---|---|---|
 | KV reuse 几乎只在 session 内 | intra 93.2% / cross 5.7% / shared 1.1% | ![](figs/f2a_reuse_topology.png) |
-| Session 极度偏斜 | replay 上 top 1% / 5% / 10% = 24% / 62% / 76% input mass（production 全 trace 更陡，top 1% = 46.5%） | ![](figs/f2b_session_skew.png) |
+| Session 极度偏斜 | production trace 上 top 1% / 5% / 10% / 25% / 50% = **46.5% / 66.5% / 74.6% / 87.5% / 96.0%** input mass | ![](figs/f2b_session_skew.png) |
 | 单请求 KV footprint 已经很大 | p99 = 11.8 GiB ≈ H20 12% | ![](figs/f2c_kv_footprint_cdf.png) |
 
 理论 APC 上界 = intra-session 79.6% / any-session 80.3%，差 <1pp。**任何不 affinity 的调度都丢绝大部分 reuse。**
@@ -58,7 +58,7 @@ agentic 平均请求 33.6k token 需 3.3GB KV；4P+4D / 6P+2D 在 agentic regime
 | sticky | **20.3s** | 55.4s | **34.6s** |
 | unified | **10.3s** | 37.7s | **18.0s** |
 
-机制：top 5% 的 session 占 ~62% input 量、且 hot session 数量远多于 instance 数（8 个），sticky 的 hash 绑定让 **每个 worker 都自己承接一份 hot session**，median worker 也被拖慢。Unified 用 LMetric fallback 把 cold/new session 重路由到非 hot worker，保留 7/8 worker 的速度。系统 p90 由大多数请求决定，所以 unified 几乎 2x 快。
+机制：production trace 上 top 1% 的 session 占 46.5% input 量、且 hot session 数量远多于 instance 数（8 个），sticky 的 hash 绑定让 **每个 worker 都自己承接一份 hot session**，median worker 也被拖慢。Unified 用 LMetric fallback 把 cold/new session 重路由到非 hot worker，保留 7/8 worker 的速度。系统 p90 由大多数请求决定，所以 unified 几乎 2x 快。
 
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