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[2602.16603] FlowPrefill: Decoupling Preemption from Prefill Scheduling Granularity to Mitigate Head-of-Line Blocking in LLM Serving
Summary
The paper presents FlowPrefill, a novel system designed to optimize large language model (LLM) serving by decoupling preemption from scheduling granularity, addressing head-of-line blocking issues.
Why It Matters
As LLMs become increasingly integral to various applications, efficient resource management in serving systems is crucial. FlowPrefill's approach to mitigate head-of-line blocking can significantly enhance responsiveness and throughput, making it relevant for developers and researchers in AI infrastructure.
Key Takeaways
- FlowPrefill improves goodput by up to 5.6x compared to existing systems.
- Decoupling preemption granularity from scheduling frequency enhances responsiveness.
- Operator-Level Preemption allows fine-grained execution interruption.
- Event-Driven Scheduling minimizes control-plane overhead while supporting efficient preemption.
- The system addresses diverse service level objectives (SLOs) effectively.
Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2602.16603 (cs) [Submitted on 18 Feb 2026] Title:FlowPrefill: Decoupling Preemption from Prefill Scheduling Granularity to Mitigate Head-of-Line Blocking in LLM Serving Authors:Chia-chi Hsieh, Zan Zong, Xinyang Chen, Jianjiang Li, Jidong Zhai, Lijie Wen View a PDF of the paper titled FlowPrefill: Decoupling Preemption from Prefill Scheduling Granularity to Mitigate Head-of-Line Blocking in LLM Serving, by Chia-chi Hsieh and 5 other authors View PDF HTML (experimental) Abstract:The growing demand for large language models (LLMs) requires serving systems to handle many concurrent requests with diverse service level objectives (SLOs). This exacerbates head-of-line (HoL) blocking during the compute-intensive prefill phase, where long-running requests monopolize resources and delay higher-priority ones, leading to widespread time-to-first-token (TTFT) SLO violations. While chunked prefill enables interruptibility, it introduces an inherent trade-off between responsiveness and throughput: reducing chunk size improves response latency but degrades computational efficiency, whereas increasing chunk size maximizes throughput but exacerbates blocking. This necessitates an adaptive preemption mechanism. However, dynamically balancing execution granularity against scheduling overheads remains a key challenge. In this paper, we propose FlowPrefill, a TTFT-goodput-optimized serving system that resolves this conflict b...