PolyServe: Efficient Multi-SLO Serving at Scale

• URL: http://arxiv.org/abs/2507.17769v1
• Date: Thu, 17 Jul 2025 05:54:42 GMT
• Title: PolyServe: Efficient Multi-SLO Serving at Scale
• Authors: Kan Zhu, Haiyang Shi, Le Xu, Jiaxin Shan, Arvind Krishnamurthy, Baris Kasikci, Liguang Xie,
• Abstract summary: PolyServe is a novel multi-SLO scheduling policy at scale that maintains high SLO attainment while maximizing throughput.<n> PolyServe achieves 1.23x goodput gain compared to existing policies, achieving up to 92.5% of optimal goodput.
• Score: 6.147741784378271
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Advances in Large Language Models (LLMs) have led to a surge of LLM-powered applications. These applications have diverse token-generation latency requirements. As a result, simply classifying workloads as latency-sensitive (LS) or best-effort (BE) overlooks the nuances within the latency-sensitive category and results in suboptimal user experiences and scheduling opportunities. However, efficiently serving requests with multiple SLO requirements poses significant challenges. First, all requests within a batch generate new tokens simultaneously, which can misalign them with their distinct SLO requirements. Moreover, while existing systems focus on auto-scaling for handling various overall request rates, the diversity of SLOs necessitates fine-grained auto-scaling among these SLO tiers. Finally, unlike LS/BE scenarios, where BE requests can be aborted at any time to ensure the SLO attainment of LS requests, those with different latency-sensitive SLOs cannot tolerate prolonged delays, and tail latency must be controlled. To tackle these challenges, we propose PolyServe, a novel multi-SLO scheduling policy at scale that maintains high SLO attainment while maximizing throughput. PolyServe first groups requests into multiple bins based on their per-token latency requirement, then schedules each bin to a subset of the server fleet. PolyServe routes requests to the highest-load but still SLO-attainable server to create a load gradient that facilitates auto-scaling. To increase utilization, PolyServe permits looser-SLO requests to share tighter-SLO instances when their own servers are saturated. PolyServe uses profiling data to guide scheduling decisions and manage tail latency through request-wait-time-aware scheduling, dynamic chunking, and continuous chunked prefill prediction. PolyServe achieves 1.23x goodput gain compared to existing policies, achieving up to 92.5% of optimal goodput.

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