Adaptive Rescheduling in Prefill-Decode Disaggregated LLM Inference

• URL: http://arxiv.org/abs/2510.13668v1
• Date: Wed, 15 Oct 2025 15:29:08 GMT
• Title: Adaptive Rescheduling in Prefill-Decode Disaggregated LLM Inference
• Authors: Zhibin Wang, Zetao Hong, Xue Li, Zibo Wang, Shipeng Li, Qingkai Meng, Qing Wang, Chengying Huan, Rong Gu, Sheng Zhong, Chen Tian,
• Abstract summary: Large Language Model (LLM) inference has emerged as a fundamental paradigm.<n>In this paper, we propose ARES, an adaptive decoding rescheduling system powered by length prediction to anticipate future workloads.
• Score: 29.19884207604898
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large Language Model (LLM) inference has emerged as a fundamental paradigm. In real-world scenarios, 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 ARES, an adaptive decoding 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 74.77% and achieving up to 2.24 times higher goodput.

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