Justitia: Fair and Efficient Scheduling for LLM Applications

• URL: http://arxiv.org/abs/2510.17015v1
• Date: Sun, 19 Oct 2025 21:34:34 GMT
• Title: Justitia: Fair and Efficient Scheduling for LLM Applications
• Authors: Mingyan Yang, Guanjie Wang, Manqi Luo, Yifei Liu, Chen Chen, Han Zhao, Yu Feng, Quan Chen, Minyi Guo,
• Abstract summary: We design Justitia, a novel scheduler with three key techniques.<n>Justitia models the service cost of LLM applications in a memory-centric manner.<n>It uses a simple neural network model to conduct light-weight and also accurate demand prediction.
• Score: 32.900257208449716
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the era of Large Language Models (LLMs), it has been popular to launch a series of LLM inferences -- we call an LLM application -- to better solve real-world problems. When serving those applications in shared GPU servers, the schedulers are expected to attain fast application completions with guaranteed worst-case performance. However, mainstream LLM schedulers fail to behave well for LLM applications -- due to head-of-line blocking or over-constrained resource allocation. In this paper, we propose to serve LLM applications in a fair and also efficient manner. To this end, we design Justitia, a novel scheduler with three key techniques. First, given that memory is prevalently a bottleneck for mainstream inference frameworks like vLLM, Justitia models the service cost of LLM applications in a memory-centric manner. Meanwhile, it uses a simple neural network model to conduct light-weight and also accurate demand prediction. Moreover, Justitia adopts a virtual-time based fair queuing algorithm to reduce the overall performance with guaranteed worst-case delay. We have implemented Justitia atop vLLM, and experimental results involving diverse LLM applications show that it can substantially enhance the scheduling efficiency with fairness preserved.

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