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A Focused Review of Multi-Agent Control for Data Center Cooling Systems
编号：104 访问权限：仅限参会人 更新：2026-09-28 15:30:00 浏览：2次 张贴报告

报告开始：暂无开始时间（Asia/Shanghai）

报告时间：暂无持续时间

所在会场：[暂无会议] [暂无会议段]

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摘要
Abstract—Data center cooling systems are characterized by high energy consumption, strong thermal coupling, and multiple operational constraints. Conventional centralized approaches can incur substantial implementation costs and may suffer from inadequate robustness and limited global coordination in large-scale facilities. Multi-agent control represents cooling towers, rack fans, and other components as agents with local sensing, decentralized decision-making, and cooperative communication capabilities; when combined with reinforcement learning, this paradigm enables the joint optimization of energy use, thermal safety, and carbon emissions. Drawing on the theoretical foundations and engineering applications of multi-agent control, this review organizes the principal research challenges into three categories: system modeling, reward design and allocation, and learning convergence. System modeling determines how the state space is represented; reward design specifies the objectives of cooperative control; and convergence behavior governs the feasibility of practical deployment. A targeted literature update covering 2013 to September 2026 was organized around these questions and we chosen work reporting cooling-related states, actions, coordination, rewards, learning constraints, or directly relevant benchmarks. Recent advances and unresolved challenges are critically reviewed to support the development of safe, energy-efficient, and scalable data center cooling systems.
Keywords—multi-agent systems, cooperative control, reinforcement learning, cooling systems, data centers

 
关键词
Keywords—multi-agent systems, cooperative control, reinforcement learning, cooling systems, data centers
报告人
Jian Cen
教授 广东技术师范大学

稿件作者
YiQiao Gao 广东技术师范大学
Jian Cen 广东技术师范大学
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重要日期

  • 会议日期

    11月06日

    2026

    至

    11月08日

    2026

  • 10月15日 2026

    初稿截稿日期

主办单位
IEEE Instrumentation and Measurement Society
承办单位
Sichuan University
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