Sep 2026· Proceedings of the 17th ACM SIGOPS Asia-Pacific Workshop on Systems· 0 citations· 41 references
TL;DR
CALM-MAS is proposed, a congestion-aware serving framework for LLM applications that treats LLM test-time computation as an elastic resource, dynamically adjusting the compute profile of admitted tasks to tame congestion.
Abstract
Agentic LLM workflows issue many dependent calls with unpredictable resource demand, causing queue buildup and latency degradation on shared serving backends when left unmanaged. In this paper, we propose CALM-MAS, a congestion-aware serving framework for LLM applications that treats LLM test-time computation as an elastic resource, dynamically adjusting the compute profile of admitted tasks to tame congestion. CALM-MAS detects early signals of back-end saturation, and leverages the flexibility of LLM applications to regulate load. During spikes of requests, the system downgrades agent topology and reasoning depth; during low-utilization periods, it allocates additional reasoning effort to maximize task accuracy. Compared with a static serving baseline based on vLLM, CALM-MAS reduces shared-backend tail latency by 77% with the accuracy degradation remaining confined to 6.1 pps relative to the native agent configuration.
PackServe is a scheduler designed to reduce resource costs while meeting latency SLOs for agentic LLM serving, which uses compact white-box models to predict latency under prefill/decode interference and packs requests onto fewer serving instances while preserving KVC reuse and SLO constraints.
Zhi-Yuan Tan, De-Jiang Zhu, Jing-Zhe Jiang et al.· 0 citations
Recent advances in large language models (LLMs) are driving the emergence of multi-modal and agentic services for mobile users through cloud and edge infrastructures, where long-context workloads pose daunting challenges for inference latency. Existing disaggregated LLM serving systems largely rely on hardware profilin...
Shi-Cong Liu, Xiang-Hao Yu, Zheng-Run Gao et al.· 0 citations
SemSpot is presented, a semantics-aware service model that allows agent applications to leverage the spot capacity of LLM inference platforms and develops the service model, economic boundary, and the cross-layer research agenda required to realize SemSpot.
This work presents AgentSysBench, a benchmark suite and measurement toolkit with ten representative agentic applications and unified systems-level instrumentation, and identifies six properties that distinguish agentic workloads from conventional LLM serving.
Chaokun Chang, Yu-Kun Zhou, Kai-Hua Fu et al.· 11 citations
CELLServe formalizes SLO-constrained joint resource provisioning as an optimization problem with a dedicated algorithm, and introduces an opportunistic instance merging strategy for decode phase functions to reclaim fragmented resources.
Ze-Jian Wang, Nan Lin, Zi-Nuo Cai et al.· ACM Transactions on Architec...· 0 citations
An online request scheduling framework for edge LLM inference that jointly minimizes long-term average end-to-end latency and regulates workload distribution across heterogeneous edge servers is investigated and the LYREO approach, a cross-slot inference model that captures transmission, prefill, iteration-level decodi...
Zhen Li, Jun Cai, Hao-Ran Gao et al.· arXiv.org· 0 citations
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