LLM Inference Under Bursty Workload Distribution: Modifying the WAIT Algorithm

Abstract:Large Language Models (LLMs) such as ChatGPT and Claude are widely used for information retrieval and problem-solving. Recent work has focused on improving scheduling algorithms to boost throughput while maintaining low latency. However, these approaches often assume Poisson request arrivals with constant rates - an assumption that fails to reflect the inherently bursty and dynamic nature of real-world traffic. We propose a lightweight extension to the state-of-the-art WAIT algorithm [1], which adapts to time-varying arrival rates without prior traffic knowledge. The proposed algorithm performs online estimation of request intensity based on observed interarrival times. Using Markov Modulated Poisson Process (MMPP)-based synthetic workloads with diverse request types, we conduct a simulation-based evaluation demonstrating that the proposed method achieves higher throughput than Sarathi-Serve [2], ORCA [3], and vLLM [4] in the evaluated low arrival-rate shift scenarios while maintaining comparable latency.

Machine Learning (cs.LG)

arXiv:2608.06135 [cs.LG]

(or arXiv:2608.06135v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.06135 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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