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arxiv: 2605.02329 · v1 · submitted 2026-05-04 · 💻 cs.DC

Recognition: 2 theorem links

· Lean Theorem

Taming Request Imbalance: SLO-Aware Scheduling for Disaggregated LLM Inference

Qipeng Wang , Zhendong Yang
Authors on Pith no claims yet

Pith reviewed 2026-05-08 18:24 UTC · model grok-4.3

classification 💻 cs.DC
keywords LLM inference schedulingSLO attainmentdisaggregated servingprefill priority schedulingdecode adaptive batchingrequest length distributiontime-to-first-tokentime-per-output-token
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The pith

Kairos improves TTFT SLO attainment by up to 24% and decode throughput by 19% in disaggregated LLM inference.

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

The paper sets out to demonstrate that an SLO-aware scheduler can overcome the problems of head-of-line blocking and straggler-induced underutilization in disaggregated LLM serving caused by long-tail request length distributions. By using predicted prefill completion times to assign urgency-based priorities instead of FCFS, and by guiding decode batching with the available slack to the TPOT SLO rather than fixed continuous batching, the system can meet more TTFT and TPOT targets while processing more requests per unit time. A sympathetic reader would care because production LLM deployments must deliver consistent response times under highly variable loads, and failures here directly affect user experience and operational costs.

Core claim

Kairos addresses request imbalance through urgency-based priority scheduling on the prefill side that dynamically selects requests based on predicted completion times to maximize TTFT SLO attainment, paired with slack-guided adaptive batching on the decode side that greedily packs short requests using the time gap to the TPOT SLO to maximize throughput while strictly meeting requirements. Evaluations on an online serving dataset with a state-of-the-art LLM show gains of up to 23.9% in TTFT SLO attainment, 27.1% in TPOT SLO attainment, 33.8% in end-to-end SLO attainment, and 19.3% in decode throughput versus state-of-the-art baselines.

What carries the argument

Kairos scheduling system consisting of urgency-based priority scheduling for prefill requests and slack-guided adaptive batching for decode requests.

If this is right

  • Urgency-based selection reduces head-of-line blocking by long prefill requests for time-sensitive queries.
  • Slack-guided batching increases decode throughput by allowing more short requests to run in parallel without SLO violations.
  • Combined mechanisms improve end-to-end SLO attainment by optimizing both phases of the inference pipeline.
  • Decode phase utilization rises as stragglers no longer dictate batch composition.

Where Pith is reading between the lines

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

  • Similar priority and slack mechanisms could be adapted for other disaggregated computing tasks with variable task sizes.
  • Improving the accuracy of prefill time predictors would directly amplify the benefits of the urgency scheduling.
  • The system might enable serving larger models or higher loads on the same hardware by raising effective throughput.

Load-bearing premise

The prefill completion time predictions must be sufficiently accurate and the evaluation dataset must accurately represent real production request patterns for the scheduling to produce the claimed improvements.

What would settle it

An experiment that replaces the prefill time predictions with random or fixed values and observes that the SLO and throughput gains disappear would show that the prediction-based prioritization is essential to the results.

Figures

Figures reproduced from arXiv: 2605.02329 by Qipeng Wang, Zhendong Yang.

Figure 1
Figure 1. Figure 1: Imbalabce request distribution and decode step time growth to sequence length. gency and adaptively batches decode requests to maxi￾mize both SLO attainment and system throughput. • We implement and evaluate Kairos on various tasks, demonstrating significant improvements in SLO attain￾ment and decode throughput. 2. Background and Motivation 2.1. LLM Serving System LLM inference is a two-phase process consi… view at source ↗
Figure 2
Figure 2. Figure 2: Workflow of the algorithms in Kairos. x-axis represents request length and the y-axis shows the cumulative distribution or probability density. The long-tail characteristic is clearly visible in both settings. Impact of request imbalance. This imbalance leads to se￾vere SLO degradation in both the prefill and decode phases. On the prefill side, when a long request arrives ahead of subsequent short requests… view at source ↗
Figure 3
Figure 3. Figure 3: End-to-end SLO attainment of Minimax M2.5 and online dataset under differnet QPS. SLOs; (4) Decode throughput quantifies the per-request decode speed in tokens per second, reflecting decode-side efficiency. SLO Configuration. We set the TTFT SLO to 8s and the TPOT SLO to 50ms. These values are chosen to re￾flect typical production requirements: the 8s TTFT SLO corresponds to a commonly acceptable initial l… view at source ↗
Figure 6
Figure 6. Figure 6: TTFT SLO attainment of Minimax M2.5 and online dataset under differnet QPS. achieves 92–97% at low QPS (2.0–2.8) and remains above 83% even at QPS=3.2. At QPS=2.0, Kairos improves TPOT attainment from 73.7% to 96.6%, a gain of 22.9%. At QPS=3.0, the gap is 27.1% (89.6% vs. 62.5%). The structural disadvantage of DistServe on TPOT arises from its continuous batching policy, which batches all ac￾tive decode r… view at source ↗
Figure 5
Figure 5. Figure 5: TTFT SLO attainment of Minimax M2.5 and online under differnet QPS. attainment, as the system is under-loaded and all requests are served promptly. As QPS climbs, DistServe degrades sharply: its TTFT attainment drops to 76.1% at QPS=3.0, 60.5% at QPS=3.4, and 15.5% at QPS=5.0. In contrast, Kairos maintains 100% TTFT attainment at QPS=3.0 and degrades more gracefully, reaching 76.9% at QPS=3.4 and 16.1% at … view at source ↗
read the original abstract

In production environments, large language model (LLM) serving is required to meet stringent service-level objectives (SLOs) amid highly variable request patterns. In practice, request lengths follow a long-tail distribution, which gives rise to head-of-line blocking on the prefill side and underutilization caused by stragglers on the decode side in disaggregated serving architectures. Current systems, which adopt first-come-first-served (FCFS) scheduling for prefill and continuous batching for decode, lack the ability to adapt to this imbalance, resulting in compromised SLO attainment and reduced throughput. To address these challenges, we propose Kairos, an SLO-aware scheduling system equipped with two complementary mechanisms. On the prefill side, Kairos employs urgency-based priority scheduling: it predicts prefill completion times and dynamically selects requests to maximize the attainment of time-to-first-token (TTFT) SLOs. On the decode side, Kairos introduces slack-guided adaptive batching, which leverages the gap between per-step decode time and the time-per-output-token (TPOT) SLO to greedily pack short requests. This approach maximizes throughput while strictly adhering to SLO requirements. We implement Kairos and conduct evaluations using an online serving dataset and a state-of-the-art LLM. Experimental results demonstrate that, compared with state-of-the-art baselines, Kairos improves TTFT SLO attainment by up to 23.9\%, TPOT SLO attainment by up to 27.1\%, end-to-end SLO attainment by up to 33.8\%, and decode throughput by up to 19.3\%.

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

2 major / 2 minor

Summary. The manuscript proposes Kairos, an SLO-aware scheduler for disaggregated LLM inference serving. It introduces urgency-based priority scheduling on the prefill side that predicts per-request completion times to dynamically prioritize requests and maximize TTFT SLO attainment, and slack-guided adaptive batching on the decode side that uses the gap between per-step decode time and TPOT SLO to greedily pack short requests while maximizing throughput. Experiments on an online serving dataset with a state-of-the-art LLM report improvements over state-of-the-art baselines of up to 23.9% TTFT SLO attainment, 27.1% TPOT SLO attainment, 33.8% end-to-end SLO attainment, and 19.3% decode throughput.

Significance. If the central claims hold, the work addresses a practically important problem in production LLM serving: head-of-line blocking from long-tail request lengths in disaggregated prefill/decode architectures. The two mechanisms are complementary and directly target the FCFS and continuous-batching limitations described. The reported gains are large enough to be relevant for system designers, and the paper ships concrete implementation and evaluation artifacts that could be reproduced.

major comments (2)
  1. [§3.1] §3.1 (urgency-based priority scheduling): The prefill mechanism sets dynamic priorities from predicted completion times, yet the manuscript provides no quantitative validation of prediction accuracy (MAE, calibration plots, or sensitivity to batch size, KV-cache contention, or hardware jitter). Because the 23.9% TTFT improvement and the claim of outperforming FCFS both depend on the predictions producing a correct priority order, large errors could invert decisions and produce worse head-of-line blocking than the baseline the paper criticizes.
  2. [§4] §4 (evaluation): The online serving dataset is used to demonstrate the gains, but no statistical characterization of its length distribution, arrival process, or comparison to production traces is supplied. Without this, it is unclear whether the 33.8% end-to-end SLO improvement generalizes beyond the specific trace chosen.
minor comments (2)
  1. [§1] The definitions of TTFT and TPOT SLOs are introduced in the abstract and §1 but never given explicit mathematical formulations (e.g., as latency thresholds per request) before being used in the mechanisms of §3; adding these would improve clarity.
  2. [Table 2] Table 2 (or equivalent results table) reports percentage improvements but does not include absolute SLO attainment rates or confidence intervals; adding these would make the magnitude of the gains easier to interpret.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and will revise the manuscript to incorporate the suggested improvements.

read point-by-point responses

  1. Referee: [§3.1] §3.1 (urgency-based priority scheduling): The prefill mechanism sets dynamic priorities from predicted completion times, yet the manuscript provides no quantitative validation of prediction accuracy (MAE, calibration plots, or sensitivity to batch size, KV-cache contention, or hardware jitter). Because the 23.9% TTFT improvement and the claim of outperforming FCFS both depend on the predictions producing a correct priority order, large errors could invert decisions and produce worse head-of-line blocking than the baseline the paper criticizes.

    Authors: We agree that the manuscript lacks quantitative validation of the prefill completion time predictions. This is a valid concern, as the urgency-based priority scheduling depends on these predictions for correct ordering. In the revised version, we will add an evaluation of prediction accuracy, including mean absolute error, calibration analysis, and sensitivity to batch size, KV-cache contention, and hardware variations. This will demonstrate that the predictions reliably support the priority decisions and the reported TTFT SLO gains. revision: yes

  2. Referee: [§4] §4 (evaluation): The online serving dataset is used to demonstrate the gains, but no statistical characterization of its length distribution, arrival process, or comparison to production traces is supplied. Without this, it is unclear whether the 33.8% end-to-end SLO improvement generalizes beyond the specific trace chosen.

    Authors: We acknowledge that the manuscript does not provide statistical characterization of the online serving dataset or comparisons to production traces. We will add this analysis in the revision, including details on request length distribution, arrival process, and relevant comparisons to publicly documented production workloads. This will better contextualize the generalizability of the 33.8% end-to-end SLO improvement and other results. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical system with experimental validation

full rationale

The paper presents Kairos as an implemented scheduling system using urgency-based priority scheduling (via predicted prefill completion times) on the prefill side and slack-guided adaptive batching on the decode side. All performance claims (TTFT/TPOT/end-to-end SLO attainment and throughput gains) are derived from direct implementation, execution on an online serving dataset, and quantitative comparison against state-of-the-art baselines. No equations, derivations, or self-citations are shown that reduce any prediction, uniqueness claim, or result to a fitted input or prior author work by construction. The central mechanisms are design choices whose efficacy is measured externally rather than defined tautologically.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim relies on the domain assumption about request distributions and the effectiveness of the proposed mechanisms, with no free parameters explicitly mentioned in the abstract.

axioms (1)
  • domain assumption Request lengths follow a long-tail distribution in production environments.
    Stated as the source of head-of-line blocking and underutilization.
invented entities (1)
  • Kairos scheduling system no independent evidence
    purpose: To address SLO issues in LLM inference
    The system is proposed in this paper.

pith-pipeline@v0.9.0 · 5589 in / 1225 out tokens · 35953 ms · 2026-05-08T18:24:01.920421+00:00 · methodology

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Reference graph

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