Efficient LLM Scheduling by Learning to Rank

• URL: http://arxiv.org/abs/2408.15792v1
• Date: Wed, 28 Aug 2024 13:35:54 GMT
• Title: Efficient LLM Scheduling by Learning to Rank
• Authors: Yichao Fu, Siqi Zhu, Runlong Su, Aurick Qiao, Ion Stoica, Hao Zhang,
• Abstract summary: We show that it is possible to predict the relative ranks of output lengths in a batch of requests, using learning to rank. We develop a novel scheduler for LLM inference and serving that can approximate the shortest-job-first (SJF) schedule better than existing approaches.
• Score: 19.33941579312897
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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