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arxiv: 2606.09061 · v1 · pith:3WE4QVGJnew · submitted 2026-06-08 · 💻 cs.DC · cs.PF

Fairness-Aware and Latency-Controllable Scheduling for Chunked-Prefill LLM Serving

Haoxin Liu , Jiayi Wang , Yueshen Xu , Rui Li This is my paper

Pith reviewed 2026-06-27 15:07 UTC · model grok-4.3

classification 💻 cs.DC cs.PF
keywords chunked-prefillLLM servingscheduling policyaging-based schedulinglatency predictionfairnesstail latencyprefill control
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The pith

Aging-based scheduling with latency prediction cuts mean LLM serving latency over 10% versus FCFS

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper aims to fix fairness and latency problems in chunked-prefill LLM serving under varied workloads by moving beyond rigid FCFS and static token budgets. It introduces an aging policy that raises priority based on how long a request has waited and how much prefill work remains. Two additional mechanisms replace fixed budgets with predicted target times and actively limit prefill concurrency. Experiments on real workloads show these changes lower average latency, shrink tail latencies, and reduce fragmentation, indicating the temporal and structural controls work together.

Core claim

The authors establish that a lightweight aging-based scheduling policy, together with Latency-Prediction-Based Request Scheduling (LPRS) and Active Prefill Control (APC), replaces FCFS and static budgets to enforce fairness and target-time constraints, producing over 10% lower mean end-to-end latency, reduced P99 tail latency, and suppressed prefill fragmentation on NVIDIA GPUs and Ascend accelerators with real-world workloads.

What carries the argument

Aging-based scheduling policy that dynamically sets priorities from accumulated waiting time and remaining prefill work, augmented by LPRS for target-time constraints and APC for active regulation of prefill concurrency.

If this is right

  • The aging policy reduces mean end-to-end latency by over 10% compared to FCFS.
  • LPRS and APC together reduce P99 tail latency.
  • The policies suppress prefill fragmentation.
  • Structural prefill control and temporal latency constraints are complementary.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The aging priority formula could be adapted to non-chunked serving engines if waiting-time and work estimates remain available.
  • Releasing the code allows independent checks on whether the latency gains persist under different hardware or traffic mixes.
  • The approach highlights a general tension between prefill chunking efficiency and request fairness that other schedulers may need to address.

Load-bearing premise

That the tested real-world workloads on NVIDIA GPUs and Ascend accelerators represent typical production heterogeneous LLM serving traffic and that gains come from the policies rather than other factors.

What would settle it

Running the same policies on a fresh workload set with markedly different request sizes or arrival patterns and finding no reduction in mean or P99 latency would falsify the central performance claim.

Figures

Figures reproduced from arXiv: 2606.09061 by Haoxin Liu, Jiayi Wang, Rui Li, Yueshen Xu.

Figure 1
Figure 1. Figure 1: The workflow of the designed Active Prefill Control mechanism. [PITH_FULL_IMAGE:figures/full_fig_p010_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Mean E2E latency and TTFT under FCFS and Aging. [PITH_FULL_IMAGE:figures/full_fig_p012_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: P95 E2E latency and TTFT under FCFS and Aging. [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: CDF of request E2E latency under FCFS and Aging. [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Sensitivity of the aging policy to the chunk size. [PITH_FULL_IMAGE:figures/full_fig_p014_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Sensitivity of Aging to the waiting-time weight. [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗
read the original abstract

As large language models (LLMs) are increasingly deployed with highly heterogeneous workloads, chunked-prefill execution has emerged as a mainstream serving architecture. Balancing scheduling fairness and latency stability in such environments is critical; otherwise, severe head-of-line blocking and request starvation will degrade user experience. However, existing systems rely on rigid First-Come, First-Served (FCFS) policies and static token budgets, leading to fairness degradation and unpredictable latency jitter. To address these issues, we propose a fairness-aware and latency-controllable scheduling framework for chunked-prefill LLM engines. Specifically, we design a lightweight aging-based scheduling policy that dynamically calculates priorities using accumulated waiting time and remaining prefill work. Furthermore, we develop Latency-Prediction-Based Request Scheduling (LPRS) and Active Prefill Control (APC) to replace static budgets with target-time constraints and actively regulate prefill concurrency. We evaluated our scheduling framework on NVIDIA GPUs and Ascend accelerators using real-world workloads. Results show the aging policy reduces mean end-to-end latency by over 10\% compared to FCFS. Moreover, LPRS and APC significantly reduce P99 tail latency and suppress prefill fragmentation, confirming that the structural prefill control and the temporal latency constraints are fundamentally complementary. All codes have been released in Github.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper proposes a fairness-aware and latency-controllable scheduling framework for chunked-prefill LLM serving. It introduces a lightweight aging-based policy that computes priorities from waiting time and remaining prefill work, plus Latency-Prediction-Based Request Scheduling (LPRS) that replaces static token budgets with target-time constraints and Active Prefill Control (APC) that regulates prefill concurrency. Evaluation on NVIDIA GPUs and Ascend accelerators with real-world workloads reports that the aging policy reduces mean end-to-end latency by over 10% versus FCFS, while LPRS+APC reduce P99 tail latency and suppress prefill fragmentation; the authors conclude the structural and temporal mechanisms are complementary. All code is released.

Significance. If the performance deltas are shown to be robust and causally attributable to the proposed policies, the work would provide a practical contribution to production LLM serving by improving fairness and tail latency under heterogeneous chunked-prefill workloads. The open-source release supports reproducibility.

major comments (3)
  1. [§5 (Evaluation)] §5 (Evaluation): The reported >10% mean E2E latency reduction and P99 improvements lack any description of the number of runs, statistical tests, error bars, or variance across trials, so the quantitative claims cannot be assessed for reliability.
  2. [§5 (Evaluation)] §5 (Evaluation): No ablation experiments are presented that hold all other factors fixed while toggling only the aging priority, LPRS target-time logic, or APC concurrency control; therefore the conclusion that the mechanisms are 'fundamentally complementary' rests on an unisolated comparison against an unspecified FCFS baseline.
  3. [§5 (Evaluation)] §5 (Evaluation): The workloads are described only as 'real-world' with no characterization of arrival burstiness, prefill-length distribution, or multi-model heterogeneity, leaving open whether the observed gains generalize or are artifacts of the particular traces and accelerator implementations.
minor comments (2)
  1. The abstract and §5 should explicitly state the exact FCFS baseline configuration (e.g., token budget, preemption rules) used for comparison.
  2. Notation for priority calculation in the aging policy (waiting time + remaining prefill work) should be formalized with an equation in §3 or §4.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback on the evaluation section. We address each major comment below and will revise the manuscript to strengthen the presentation of results.

read point-by-point responses

  1. Referee: [§5 (Evaluation)] The reported >10% mean E2E latency reduction and P99 improvements lack any description of the number of runs, statistical tests, error bars, or variance across trials, so the quantitative claims cannot be assessed for reliability.

    Authors: We agree that details on experimental repeatability and variance are necessary for assessing reliability. The experiments underlying the reported figures were repeated across multiple independent trials, but the manuscript omitted the exact count, error bars, and any statistical tests. In the revised version we will explicitly state the number of runs, include error bars (standard deviation), and report the results of basic statistical comparisons where appropriate. revision: yes

  2. Referee: [§5 (Evaluation)] No ablation experiments are presented that hold all other factors fixed while toggling only the aging priority, LPRS target-time logic, or APC concurrency control; therefore the conclusion that the mechanisms are 'fundamentally complementary' rests on an unisolated comparison against an unspecified FCFS baseline.

    Authors: We acknowledge that the current evaluation does not isolate the contribution of each mechanism through controlled ablations. The complementarity claim was drawn from the joint improvements observed when all components are enabled versus the FCFS baseline. To address the concern we will add a dedicated ablation study in the revision that toggles the aging policy, LPRS, and APC individually while holding all other factors constant. revision: yes

  3. Referee: [§5 (Evaluation)] The workloads are described only as 'real-world' with no characterization of arrival burstiness, prefill-length distribution, or multi-model heterogeneity, leaving open whether the observed gains generalize or are artifacts of the particular traces and accelerator implementations.

    Authors: The workloads are drawn from production traces, yet the manuscript provides only a high-level description. We agree that quantitative characterization of arrival patterns, prefill-length distributions, and heterogeneity would improve interpretability. In the revision we will insert a workload characterization subsection that reports the relevant statistics (e.g., inter-arrival time distributions, prefill token histograms, and model mix). revision: yes

Circularity Check

0 steps flagged

No circularity; purely empirical claims with no derivation chain

full rationale

The paper describes a scheduling framework with aging policy, LPRS, and APC, then reports measured latency reductions from evaluations on NVIDIA GPUs and Ascend accelerators using real-world workloads. No equations, fitted parameters, predictions derived from inputs, or self-citation chains appear in the provided text. All headline results (e.g., >10% mean latency reduction, P99 improvements) are presented as direct experimental outcomes rather than reductions to prior definitions or fits. This is a standard systems paper whose central claims rest on external benchmarks, not internal self-reference.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract supplies no information on free parameters, background axioms, or newly postulated entities.

pith-pipeline@v0.9.1-grok · 5769 in / 1231 out tokens · 22161 ms · 2026-06-27T15:07:58.435820+00:00 · methodology

discussion (0)

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