Efficient LLM Scheduling by Learning to Rank

Yichao Fu; Siqi Zhu; Runlong Su; Aurick Qiao; Ion Stoica; Hao Zhang

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arXiv · 2408.15792

Efficient LLM Scheduling by Learning to Rank

Yichao Fu·Siqi Zhu·Runlong Su·Aurick Qiao·Ion Stoica·Hao Zhang

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Abstract

In Large Language Model (LLM) inference, the output length of an LLM request is typically regarded as not known a priori. Consequently, most LLM serving systems employ a simple First-come-first-serve (FCFS) scheduling strategy, leading to Head-Of-Line (HOL) blocking and reduced throughput and service quality. In this paper, we reexamine this assumption -- we show that, although predicting the exact generation length of each request is infeasible, it is possible to predict the relative ranks of output lengths in a batch of requests, using learning to rank. The ranking information offers valuable guidance for scheduling requests. Building on this insight, we develop a novel scheduler for LLM inference and serving that can approximate the shortest-job-first (SJF) schedule better than existing approaches. We integrate this scheduler with the state-of-the-art LLM serving system and show significant performance improvement in several important applications: 2.8x lower latency in chatbot serving and 6.5x higher throughput in synthetic data generation. Our code is available at https://github.com/hao-ai-lab/vllm-ltr.git

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BibTeX RIS

Yichao Fu, Siqi Zhu, Runlong Su, Aurick Qiao, Ion Stoica, Hao Zhang. 2024-08-28. Efficient LLM Scheduling by Learning to Rank. https://arxiv.org/abs/2408.15792

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