Justitia: Fair and Efficient Scheduling of Task-parallel LLM Agents with Selective Pampering

Abstract

LLM agents, which often comprise parallel inference tasks, are commonly adopted to solve real-world problems. When serving such task-parallel LLM agents in shared GPU servers, the scheduler is expected to attain fast agent completion with guaranteed worst-case performance. For that objective, our insight is to selectively pampering agents based on their completion order under idealized fair-sharing. We design Justitia, a fair and also efficient scheduler for task-parallel LLM agents. Noticing that memory is prevalently a bottleneck in LLM serving, Justitia quantifies the true agent cost in a memory-centric manner. It also adopts a light-weight yet accurate method to predict agent costs. Finally, 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 agents show that it can substantially enhance the scheduling efficiency with fairness preserved.