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arXiv · 2510.13668

STAR: Decode-Phase Rescheduling for LLM Inference

Zhibin Wang·Zetao Hong·Xue Li·Zibo Wang·Shipeng Li·Qingkai Meng·Qing Wang·Chengying Huan·Rong Gu·Sheng Zhong·Chen Tian
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Abstract

Large Language Model (LLM) inference has emerged as a fundamental paradigm, however, variations in output length cause severe workload imbalance in the decode phase, particularly for long-output reasoning tasks. Existing systems, such as PD disaggregation architectures, rely on static prefill-to-decode scheduling, which often results in SLO violations and OOM failures under evolving decode workloads. In this paper, we propose STAR, a decode rescheduling system powered by length prediction to anticipate future workloads. Our core contributions include: (1) A lightweight and continuous LLM-native prediction method that leverages LLM hidden state to model remaining generation length with high precision (reducing MAE by 49.42%) and low overhead (cutting predictor parameters by 93.28%); (2) A rescheduling solution in decode phase with a dynamic balancing mechanism that integrates current and predicted workloads, reducing P99 TPOT by 75.1% and achieving 2.63 times higher goodput.

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Zhibin Wang, Zetao Hong, Xue Li, Zibo Wang, Shipeng Li, Qingkai Meng, Qing Wang, Chengying Huan, Rong Gu, Sheng Zhong, Chen Tian. 2025-10-15. STAR: Decode-Phase Rescheduling for LLM Inference. https://doi.org/10.1145/3806645.3807813

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