MAPS: Memory-Aware Predictive Scheduling Framework for Large Language Model Serving

Abstract:The surge of large language model (LLM) applications on personal devices imposes massive, bursty workloads on cloud serving infrastructure. While prefill-decode disaggregation improves throughput and scalability, memory-bound decode instances often suffer from persistent load imbalance, as output lengths are unknown when requests arrive at the cloud. To address this, we propose MAPS, a Memory-Aware Predictive Scheduling framework tailored for disaggregated LLM serving. MAPS performs device-assisted speculative output length prediction overlapped with cloud-side prefilling, incurring negligible latency overhead. To handle generation uncertainty, MAPS applies uncertainty-aware calibration to derive output-length upper bounds with target coverage, enabling safe scheduling decisions. Building on these bounds, MAPS employs a hierarchical global-local scheduling strategy to mitigate inter-decoder queue buildup and intra-decoder head-of-line blocking. Extensive experiments on two real-world workloads and two LLMs show that MAPS significantly outperforms three state-of-the-art systems, reducing average end-to-end latency by 42.6 and tail latency by up to 84.8.

18 pages, 14 figures. Accepted at ICML 2026

Artificial Intelligence (cs.AI)

arXiv:2609.15359 [cs.AI]

(or arXiv:2609.15359v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.15359 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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