New analysis/characterization/elastic_migration_v2/ packages the
unified_v2 + unified_kv_both experiments into a self-contained
results section that the paper can cite as the "we tried selective
PD-sep migration" case study. The section finds three independent
reasons PD-sep doesn't help on agentic w600:
1. Mooncake kv_both substrate alone (no PD-sep ever firing) imposes
TTFT p90 +45%, TPOT p90 +25%, hotspot index +19% vs plain
unified. Per-step KVConnectorMetadata maintenance and block
reservation semantics dominate even when no transfer is pending.
2. PD-sep gate fires only 0.16-0.41% of requests across two
gate-tightness configurations. 88-76% are killed by
new_local < threshold because 93% intra-session reuse on agentic
traces leaves a small uncached tail; 19% are killed by
chosen_no_active_decode (snapshot-time gate). Even relaxed
thresholds can't grow trigger rate past 0.5%.
3. When PD-sep fires, the calibrated cost model
(0.3s + bytes / 2.7 GB/s) is wrong by 10-20x. 5 triggered
requests in v2.1 saw realized TTFT 12-45s vs model-predicted
migrate cost 0.7-2.2s, consistent with the E2 audit's finding
that D-side block pre-reservation and missing layerwise
pipelining dominate the decode_sent -> first_token clock.
Three-way comparison (unified vs unified_kv_both vs unified_v2):
v2 vs the kv_both control is roughly net-zero (-10% hotspot,
-14% TPOT p90, +3% TTFT p90, +9% TTFT p99). v2 vs plain unified is
strictly worse by 27-49% across latency percentiles because the
kv_both substrate tax is unavoidable when the policy is enabled.
Contents:
- README.md: the four results sections, the three-way comparison
table, an explicit "what this claims for the paper" list, and a
cross-reference index to the earlier characterization documents.
- data/: b3_policy_comparison.json + per-policy breakdown.json
+ per-policy hotspot_index.json for the four policies in scope.
- figures/: 4 PNGs rendered by render_figures.py:
* fig_kv_both_overhead.png — 4-metric bar chart with delta
annotations showing kv_both alone costs +45% TTFT p90.
* fig_v2_trigger_funnel.png — per-reason request count for the
two gate configurations on log scale.
* fig_v2_predicted_vs_actual.png — scatter of model-predicted
migrate cost vs realized TTFT for the 5 triggered requests,
with y=x, 10x, and 20x reference lines.
* fig_three_way_hotspot.png — per-worker TTFT p90 grouped bars
across the three policies.
The section is intentionally self-contained: it lists what the
experiment validates (cost model picks correct candidates;
shadow-drift fix is necessary; same-worker interference is real)
alongside what it disproves (per-request PD-sep on agentic via
Mooncake is not a net win in current implementation).
Refs: E1/E2 subagent audits, B2 microbench, unified_v2 commits
19f69a9 / 4b833d3 / 95c8ef8.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
User's 2026-05-25 draft aligning three threads (agentic-kv vLLM
experiments, dash0 artifacts, agentic-pd-hybrid SGLang work) into
a single story for the paper. Tracked so future iterations and
review history are in version control.
Co-Authored-By: Gahow Wang <chiahaco@gmail.com>
After the B3 audit bug fixes (joined_analysis hotspot median +
b3_analyze percentile interp), regenerate b3_policy_comparison.json
and the per-policy hotspot_index.json from the same raw run on
dash0 and re-render the three affected figures (apc-vs-hotspot,
latency-bars, per-worker TTFT).
Key number changes in window_1_results.md:
- hotspot_index magnitudes corrected (all five policies; lmetric
smallest delta at +0.7%, sticky largest at +16.1%)
- "capped reduces hotspot 13%" -> "~10% (2.253 -> 2.020)"
- TTFT/E2E/TPOT percentiles shift by <1% from floor->interp
(unified TTFT p90 7.24 -> 7.35 s)
Restructured "Caveats" into "Limitations (read this before quoting
B3 numbers)":
1. Agentic dispatch coupling is by design — promoted from caveat
to top-level methodology framing, tied to
agentic_dispatch_coupling.md
2. B3 interference_index is binary (not size-graded) — added
3. Hot-sweep cache contamination (<1%) — kept
4. Unified interference unrecoverable — kept with explicit warning
not to read unified's failure attribution as causal
5. w600 is a sample, not full trace — kept
6. Reuse decomposition is per-token in expectation — added
current_results/characterization_claim_matrix.md updates:
- The "heavy-tail not sole cause" claim now cites the corrected
~10% drop with the median bug noted
- New supported claim: "B3 saturated-replay latency gaps include an
agentic dispatch-coupling feedback term, which is intentional and
matches production"; cited against agentic_dispatch_coupling.md.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Three fixes from the B3 audit:
1) joined_analysis.hotspot_index used sorted[n//2] as median, which
returns the ~60th percentile for n=8 (even-length). Systematically
under-states the hotspot index. Recomputed values:
lmetric 2.238 -> 2.253 (+0.7%)
load_only 1.140 -> 1.294 (+13.5%)
sticky 2.349 -> 2.728 (+16.1%)
unified 3.350 -> 3.667 (+9.5%)
capped 1.937 -> 2.020 (+4.3%)
Qualitative ranking preserved; "capped only modestly reduces hotspot"
story holds with ~10% drop instead of the previously reported 13%.
Added test_hotspot_index_uses_true_median_for_even_n to lock in the
fix.
2) b3_analyze.sh's pct() helper used floor-indexed percentile
sorted[int(p*(n-1))], inconsistent with metrics._percentile and
joined_analysis._percentile which both use linear interpolation.
Now matches.
3) b3_sweep.sh's capped step called run_policy "capped", but the
proxy's argparse has no "capped" choice, so the hot-sweep variant
would have crashed on this step. The actual capped data was
produced via b3_isolated_policy.sh with --policy lmetric. Replace
the broken inline call with an explicit launch_proxy lmetric +
inline replayer block so the sweep script matches the data path
it documents.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The B3 audit flagged the trace replayer's "fire turn N+1 immediately
if turn N is behind schedule" semantics as a potential benchmark
crime, because under saturation the effective arrival process becomes
policy-dependent (slow policy -> longer session lifetimes -> more
concurrent in-flight -> harder system -> still slower). The audit
called this dispatch slip.
But in agentic workloads, turn N+1 is generated by a tool-call
response or an autonomous-loop step, not by a human reading the
previous reply. There is no inter-turn think-time. So the replayer's
"no think-time, sequential within session, fire-immediately-when-
ready" behavior is the correct model of agentic production, and the
feedback amplification is a real property of production systems
under saturation rather than an artifact of the replayer.
The note (analysis/characterization/agentic_dispatch_coupling.md)
lays out:
- The dispatch rule and the apparent feedback loop
- Why agentic workloads do not have user think-time
- Application of Little's Law: slower policy carries higher concurrent
in-flight load, so the policy x feedback gap is real, not artifact
- Reframes B3 as the "production-replay" experiment and B4 as the
orthogonal "controlled-load" experiment, complementary not
hierarchical
- Calls the feedback amplification itself out as a finding worth
reporting (e.g. unified's ~2x latency-p90 gap over lmetric in B3
reflects both the routing improvement and the in-flight reduction)
- Contrasts with chat workloads (human think-time partially breaks
the feedback loop, agentic removes that floor)
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The B2 same-worker TPOT p90 idx is non-monotone: 7.89x at 32k drops
to 2.26x at 65k. The naive reading is "interference gets weaker for
huge prefills"; the actual mechanism is a regime shift, and reading
TPOT p90 alone is misleading.
Three superimposed effects:
1. Cost migration TPOT -> TTFT. A 32k prefill is short enough that
chunked-prefill keeps interleaving decode steps, so overlapping
decodes trickle tokens out at painful per-token rates. A 65k
prefill is long enough that overlapping decodes are *fully*
blocked for ~10s; once they break through, the injection is
winding down and subsequent iterations run unobstructed. The
cost lands on the TTFT clock (14s) instead of inflating TPOT.
2. Bimodal TPOT distribution. At 65k overlap, decodes split into
"blocked entire prefill then normal rate" and "trickled slowly
through prefill chunks". p99 sits on the second population and
grows 59 -> 169.5 ms; p90 sits on the first and shrinks.
3. "Clean" stops being clean. With 4x ~10s injections in 60s, the
110 "clean" decodes at 65k are squeezed into 2-3s recovery
pockets. TPOT p90 clean rises 6.9 -> 9.6 ms (40%), shrinking
the denominator of the ratio.
window_1_results.md adds a new B2 subsection laying out the
mechanism with the per-cell data table and the explicit reading
rule: headline interference metric is TTFT idx (monotone); TPOT
p99 is the right tail indicator; TPOT p90 alone is unsafe across
regime shifts. Direct implication: TTFT and TPOT need separate
SLO thresholds under PD-colo, because they measure costs from
different points in the request lifecycle and the cost migration
between them is workload-dependent.
current_results/characterization_claim_matrix.md adds a new
supported claim for the cost migration, listed against the existing
B2 evidence. current_results/reviewer_risk_register.md adds a
low-severity entry warning future readers off TPOT p90 alone.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Refresh the standing audit package now that B1' / B2 / B3 are complete.
current_results/characterization_claim_matrix.md
Flips seven entries from "not_yet_supported" / "partially_supported"
to "supported" with pointers into window_1_results/. New entries
cover per-session sequentiality, KV per request, real reuse
decomposition, theoretical APC ceiling, the LMetric locality gap,
Unified breaking the locality-vs-latency tradeoff, B2 causal
interference proof, sticky's interference inflation, and the
partial heavy-tail / hot-spot story. B4 SRR + B5 attribution stay
"not_yet_supported" (Window 2 work).
current_results/main_claim_allowed_runs.md
New "Allowed For Routing-Policy Comparison" section pins the five
B3 policy directories. New "Allowed For PD-colo Interference"
section pins the B2 sweep. Legacy section retained for the
pre-instrumentation 200/500/1000-req runs.
current_results/reviewer_risk_register.md
Marks the two old "high"-severity risks (sequentiality / reuse
decomposition) as resolved; adds new entries for the APC
contamination empirics, the b3_analyze.sh truncate-write bug that
cost unified's interference index, the GPU-0 EngineCore ghost
cleanup, the saturated-replay caveat for trace-timestamp dispatch,
and the synthetic B2 decode workload.
current_results/all_figures_index.md
Adds the 8 new Window 1 figures alongside the existing 6 from the
legacy summarize_runs run.
current_results/reproduction_commands.sh
Records the full B3 + B2 + figure pipeline.
analysis/characterization_todo_for_interns.md
Updates the Progress Snapshot table: B0, B1, B2, B3, B6 all DONE;
only B4 and B5 remain (Window 2).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
analysis/characterization/window_1_results.md is the headline write-up
for Window 1: workload characterization (KV per request, real reuse
decomposition, APC theoretical ceilings), B3 5-policy sweep with
per-policy interpretation, B2 same-vs-different-worker interference
microbench with causal reading, and an explicit list of what Window 1
does *not* answer (deferred to B4 SRR sweep + B5 attribution).
Under window_1_results/:
- 5 raw result JSONs from the B3 sweep, the B2 microbench, the APC
upper bound, and the KV footprint
- per-policy hotspot_index.json snapshots so render_window1_figures.py
can plot per-worker TTFT p90 distributions
- 8 PNG figures (figures/) covering the headline claims
Three takeaways the figures pin down:
1) intra-session reuse dominates (93.2%), so session-affinity routing
is the right primary lever
2) unified hybrid affinity hits 79.4% APC (97% of the 79.6% intra-
session ceiling) AND cuts TTFT p90 from lmetric's 15.6s to 7.24s
3) B2 different-worker control sits at idx ≈ 1.0 across 32× prefill-
size variation; same-worker TTFT idx scales 2.15× -> 218×, which
is the cleanest causal evidence for same-worker prefill-decode
interference
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Three CPU-only analysis pieces that turn raw Window 1 artifacts into
publishable numbers and figures.
scripts/compute_apc_upper_bound.py
Block-level trie walk over hash_ids to compute the theoretical APC
ceiling on a trace, decomposed into intra-session / any-session /
shared-prefix-only. Gives a fixed reference for what each routing
policy could *possibly* achieve. w600 result: 79.6% intra-session,
80.3% any-session, 0.1% shared-prefix.
analysis/characterization/b2_sweep_analysis.py (rewrite)
Previous version used joined_analysis.interference_index() which
labeled overlap = "any prefill in any other request during this
decode". With short-prompt decode load this is always true
(everyone's prefill overlaps everyone else's decode); n_overlap
was 239/240 even in the different-worker control.
New version labels overlap iff the decode's [t_first_token, t_finish]
intersects an actual large *injection* window, computed from the
cell's "prefill"-tagged metric rows. Different-worker control now
cleanly sits at idx ≈ 1.0, same-worker scales monotonically.
analysis/characterization/render_window1_figures.py
Renders 8 PNGs from the result JSONs: B3 latency / APC vs ceiling
/ APC vs hotspot scatter / per-worker TTFT / failure breakdown,
B2 TPOT and TTFT curves (overlap vs clean and idx), reuse
decomposition, KV footprint.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Documents each pick_instance_* function from cache_aware_proxy.py in
pseudocode so the policy semantics can be cited without re-reading
implementation details. Covers lmetric (main baseline), load_only
(no cache / no affinity control), sticky (hard affinity control),
unified (gated affinity + LMetric fallback), and capped (lmetric on
a per-session turn-capped trace).
Includes a decision matrix that maps each policy to whether it uses
session affinity, cache awareness, load awareness, and overload
break, plus a one-liner per control explaining what comparison
isolates which factor.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
scripts/b2_interference.py is the controlled microbench. It runs two
coroutines against the open proxy bypass (direct vLLM endpoints):
- decode_load: continuous short-prompt requests at fixed QPS into a
designated decode instance, to keep it decode-saturated.
- prefill_injections: N large one-token requests at fixed interval,
pointed at either the same instance (same-worker variant) or a
paired one (different-worker control).
Each cell (variant × prefill_size) gets its own metrics.jsonl plus a
run_window.json containing t_start_unix/t_end_unix. The shared
engine_*.jsonl from the scheduler patch is sliced by that window in
the aggregator.
analysis/characterization/b2_sweep_analysis.py walks the cell tree,
slices the per-worker step log by each cell's window, runs the A5
interference_index() against the slice, and emits a single
b2_sweep_summary.json with one row per cell. This is what feeds the
"interference vs uncached prefill size" figure.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Smoke validation on dash0 surfaced three real bugs that broke
interference and failure-attribution labels end-to-end:
1. endpoint_url in metrics is the proxy URL (e.g. http://h:9200);
the vLLM worker URL lives in breakdown's routed_to. The
interference index and label path were taking endpoint_url first,
so every request looked routed to a non-existent worker and the
overlap counter stayed at zero.
2. _normalize_worker hard-coded base port 8000, so a smoke run on
port 9100 resolved to engine_1100 instead of engine_0. Added a
--worker-map URL=engine_id CLI flag and _resolve_worker() that
prefers the explicit map and falls back to the heuristic.
3. vLLM rewrites the per-step rid as cmpl-<proxy_id>-<i>-<hash>, so
the str equality check between per_req rid and our proxy
request_id never matched -> every prefill step looked like
"other request prefill", which would have flipped overlap to
100%. Added _vllm_rid_matches() that strips the cmpl-/chatcmpl-
prefix.
After the fix, the same smoke run reports interference_index = 22.9
across 24 overlap / 6 clean requests on a single instance, which is
the expected shape for serial dispatch into a cold engine.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Captures 5 runs from the experiment matrix (combined-ca x3 seeds,
pdsep-4p4d seed1, pdsep-6p2d seed1) on traces/w600_r0.0015_st30.jsonl
with cuda graphs enabled. The headline:
combined-ca: TTFT p50 0.91s success 99.5%
pdsep-4p4d: TTFT p50 62.8s success 52% (69x worse, half dropped)
pdsep-6p2d: TTFT p50 51.1s success 68% (56x worse, third dropped)
C2 (fig_c2): headline bars per config with error bars.
C3 (fig_c3): per-instance KV utilization time-series. Both PD-sep
splits hit the memory wall, but the side differs by P:D ratio --
4P+4D pins the P-side, 6P+2D pins both sides (D-side back-pressures
P-side).
C4 (fig_c4): TTFT stacked breakdown. 99% of PD-sep TTFT is P-side
prefill compute; D-side wait + first token is <=1.2s. The bottleneck
is P-side prefill queueing, not D-side decode wait as the original
analytical model assumed.
system_analysis.md gains a Layer 5b that reconciles the analytical
KV-wall model (which considered D-side only) with the empirical
finding that the wall hits whichever side has fewer GPUs, and
co-saturates both at extreme splits via D-side back-pressure.
plot_pd_matrix.py ingests outputs/pd_matrix/* into all four figures.
bench.sh gained AGENTIC_STEP_LOG_DIR hooks for future runs (set during
this work but not used by the current matrix's data).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
New analysis/characterization/joined_analysis.py joins replayer
metrics.jsonl + proxy breakdown.json + worker_state.jsonl by
request_id, plus engine_*.jsonl by worker_id, and emits:
- joined.jsonl per-request merged record
- reuse_decomposition.json real intra/cross/shared classification
using session_id + hash_ids + cached_tokens
- interference_index.json TPOT_p90(same-worker prefill overlap)
/ TPOT_p90(clean), per Batch 2
- hotspot_index.json max/median worker TTFT-p90, per Batch 3
- failure_label.jsonl per-slow-request cause label, per Batch 5
- failure_breakdown.json label histogram
- window_summary.json SRR warmup/steady/drain aggregates
Closes the analyzer side of Phase A; replaces the
status: unavailable placeholders the existing scaffold emits when
join sources are missing.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- Add Progress Snapshot table to the intern TODO so per-batch status
(DONE / partial / blocked-on-instrumentation) is visible at a glance.
- New analysis/claude_characterization_work_plan.md scopes the Phase A
instrumentation tasks (A1-A5) plus Window 1 (B1'+B2+B3) and Window 2
(B4+B5) on dash0, with locked decisions for model, topology, trace,
SLO style, and GPU phasing.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Adds the experiment harness that gates the empirical claims (C2/C3/C4/C5)
in the PD-sep paper section. Three pieces:
1. scripts/bench.sh: new --mode pdsep with --pd-ratio P:D, and an
--eager flag to re-enable --enforce-eager for the cuda-graph
ablation. pdsep reuses the elastic-mode Mooncake kv_both launch and
swaps the proxy command from --combined to --prefill/--decode.
baseline and elastic flows are unchanged.
2. analysis/pd_sep_paper_section/scripts/bench_pd_matrix.sh: matrix
driver that runs {combined-ca, pdsep-4p4d, pdsep-6p2d} x cudagraph
x 3 seeds by default (~2 h on dash0). --with-rr adds combined-rr;
--with-eager doubles to ~5 h with the cuda-graph ablation. Skips
completed runs, captures per-instance vLLM logs (needed for C3
step-level KV-utilization mining).
3. fig_kv_memory_wall.pdf: empirical anchor (star) at REPORT.md §3.3's
observed 6P+2D 97% KV utilization. The marker lands on the model's
predicted curve at p90 input, confirming the steady-state analysis.
README updated with the run command, output layout, and the followup
plotters that consume outputs/pd_matrix/.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Adds the system-level argument resolving the roofline/PD-sep paradox.
Even at 95% cache reuse prefill stays compute-bound (the C6 roofline
fact), yet PD separation regresses TTFT 72%. The new system_analysis.md
walks through six layers showing why the roofline claim is necessary
but not sufficient, with the falsifiable condition being decode-side
KV memory budget: concurrent_decode * KV_per_req / (N_D * HBM_pool).
For chatbot this ratio is << 1 at any layout; for agentic at p90+
context it goes >> 1 under 4P+4D and 6P+2D, predicting the empirical
97% decode KV occupancy. fig_kv_memory_wall.pdf visualizes the model
with audit-able constants; fig_c1a/b ground the per-request KV-size
inputs in the actual sampled trace (input p50=33.5k, p90=101k,
intra-session reuse 79.2%).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Adds analysis/pd_sep_paper_section/ as the home for the "PD separation is
net negative under agentic workloads" paper section: plot scripts for C1
(workload chars), C6 (roofline), C7 (routing-vs-PD-sep lever), the C6/C7
PDFs already rendered, and a README mapping candidate claims to required
figures plus open re-run items.
Removes --enforce-eager from bench.sh and all active launch scripts so
cuda graphs are captured -- the prior methodology suppressed one of
PD-sep's structural advantages (D-node fixed-shape decode). Legacy
scripts under scripts/legacy/ are intentionally untouched as historical
records.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Per analysis/unified_routing_fix_review.md #2, several docs still
presented the retired single-argmin + PUSH-migration design as the
final algorithm. Mark them superseded and document the current hybrid
direction (commit 255c8e6).
- REPORT.md §1.1 / §3.9: add errata callout and section header noting
the "Final Design" framing was retired after cc6e562 / 4c583f2;
point readers to docs/migration-policy-design.md.
- docs/migration-policy-design.md: rewrite. Opens with the current
hybrid algorithm (LMetric base + cache_ratio>0.5 affinity gate +
tie-breaker), then a "What Was Retired" commit table, then the old
Approach A numbers preserved as "Historical Baseline-Mode Comparison".
- analysis/research_findings.md §2.2 / §5: correct the LMetric framing.
LMetric isn't "neutralized by affinity constraints" (pure --policy
lmetric has no affinity at all); it converges to similar placements
because P_tokens includes new_uncached_tokens, giving it implicit
soft affinity.
- analysis/elastic_hypotheses.md: same LMetric correction in the
"DOESN'T work" summary, plus a footer cross-referencing the current
routing direction.
- analysis/unified_routing_fix_review.md: track this file (was
untracked); it is the review handoff cited from the updated docs.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Key finding: at 16 concurrent sessions (2 per GPU), TPOT p90 degrades
from 0.073 to 0.106 (+45%), with MEDIUM TPOT at 0.197 (+149%).
This is the first time we've reproduced real prefill-decode interference
in controlled experiments.
Elastic RDMA at 16 sessions doesn't help: only 13/500 offloaded (cache-gate
correct for cold turn-1), kv_both adds ~16% TPOT overhead at high concurrency.
Load scaling: 1000req_ts20, 200req_ts10, 200req_ts5, 500req_ts10 all show
~30% GPU util at 8 sessions. The bottleneck is max_inflight_sessions, not
arrival rate.
Updated elastic_hypotheses.md with H8, H9, and comprehensive final analysis.
The real bottleneck is vLLM's chunked prefill scheduling, not routing or
PD disaggregation.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
H7: Sweeping OVERLOAD_FACTOR (2.0/1.5/1.3/1.0) has no effect on GPU
imbalance (~3.5-4x across all settings). Root cause: imbalance is from
workload skew at session placement (turn 1), not from routing at turn 2+.
H4 GPU profiling confirms: GPU balance improvement IS real (4.0x→2.0x),
and it directly improves HEAVY_COLO TTFT by 10.5%. But RDMA-offloaded
requests have bimodal transfer times (0.6s or 18-31s) that negate the
routing benefit.
Updated elastic_hypotheses.md with H7 results and next directions:
higher load experiments where contention amplifies routing differences.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Tracks all hypotheses tested during elastic PD disaggregation research:
- H1 (kv_both overhead): REJECTED — zero overhead at idle
- H2 (PS cold prefill): REJECTED — PS slower than cached C
- H3 (C_s+flexD): PARTIALLY VALIDATED — E2E -9% but HEAVY p90 +117%
- H4 (cache-aware offload): TODO — only offload high-cache-hit HEAVY
- H5 (RDMA overhead): TODO — Mooncake lacks layerwise transfer
- H6 (session migration): TODO — verify D's APC after migration
Key insight: offload decision should be cache-aware (new_tokens),
not size-based (total_input). 80k request with 90% cache = 8k prefill.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Design: offload HEAVY prefill only when P instance is less loaded than D
AND P is not overloaded (< 1.5x avg). Preserves session-sticky on D
for future KV reuse. External KV correctly registered in prefix cache.
Result (67/200 processed, 75% success):
TTFT p50: 0.551s (-49% vs baseline 1.080s)
TTFT p90: 4.135s (vs baseline 9.410s, -56%)
TPOT p90: 0.074s (same as baseline)
E2E p50: 2.938s (-45% vs baseline 5.306s)
25% error rate from ReadTimeout on very large HEAVY requests queuing on P.
Needs stricter elastic gate or higher timeout. But successful requests
show significant improvement over both baseline and previous P2P.
Also: added external_prefix_cache metrics tracking to replayer summary.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Root cause of 10.1pp APC gap: multi-turn sessions' KV evicted between
turns by cold-start prefills (66% of loss). Inter-turn gap is only 2
requests p50, but LRU cache (550 blocks) can't protect 93 blocks/session
across 14-21 concurrent sessions.
Three approaches designed:
A. Session-sticky routing with KV reservation (proxy-only, no vLLM change)
B. Two-tier KV cache: GPU + DRAM offload via Mooncake
C. Prefill-aware eviction (LFU/ARC instead of LRU, vLLM patch)
Next: simulate LRU vs LFU vs "infinite-for-MT" to quantify upper bounds,
then implement Approach A (lowest effort, immediate benchmark).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
All 8 GPUs stay PD-combined. Global scheduler classifies requests as
WARM/MEDIUM/HEAVY based on estimated new tokens after prefix cache.
Only HEAVY requests (20%, cold start >20k new tokens) get offloaded;
80% of requests are co-located with zero KV transfer.
This avoids the KV cache memory wall (no decode concentration) while
isolating heavy prefills from decode when needed.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Systematic study of prefill-decode disaggregation for agentic LLM workloads
using production GLM-5.1 coder trace (2.1M requests, 71B input tokens).
Key findings:
- Cache-aware routing improves TPOT p90 by 15% and APC from 20.8% to 44.7%
without PD separation, matching PD-Sep's decode isolation benefit
- PD separation adds +72% TTFT overhead (KV transfer) with no TPOT gain
when using the same cache-aware scheduler
- Prefill remains compute-bound even at 95% KV cache reuse (AI >1000x
vs decode AI <2), but absolute FLOPs drop 71% from cache hits
- For agentic MoE workloads, cache-aware routing > PD separation
Infrastructure:
- Trace sampler preserving session structure + hash_ids for prefix sharing
- Async trace replayer with streaming TTFT/TPOT/E2E measurement
- Unified cache-aware + token-level load-balanced global scheduler proxy
supporting both PD-colocated and PD-disaggregated (Mooncake/RDMA) modes
- vLLM 0.18.1 scheduler patch for KV transfer abort race condition
- Roofline analysis tool for prefill/decode compute characterization
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
