用强化学习动态调度工作负载与液冷，降低数据中心集群碳排放。
Hierarchical Multi-Agent Framework for Carbon-Efficient Liquid-Cooled Data Center Clusters
- 分层强化学习框架，同步优化跨集群工作负载与液冷系统。
- 结合气象、碳强度等变量，实现多数据中心协同减排。
- 提供可复现的基准仿真，适合可持续计算研究者使用。
降低云计算的环境影响需在地理分布的数据中心集群（DCC）间高效分配工作负载，并在单个数据中心内通过时间迁移工作负载来优化液冷与空调（HVAC）系统。本文提出Green-DCC，一种基于强化学习（RL）的分层控制器，可动态优化整个DCC中的工作负载与液冷配置。该系统综合考虑天气、碳强度及资源可用性等现实约束与相互依赖关系。我们展示了系统如何同步优化多个数据中心，支持数字孪生应用；并对比了不同强化学习方法在碳排放与可持续性指标上的表现，同时提供了适用于更广泛机器学习可持续性研究的框架与基准仿真。
原文摘要 · Abstract (English)
Reducing the environmental impact of cloud computing requires efficient workload distribution across geographically dispersed Data Center Clusters (DCCs) and simultaneously optimizing liquid and air (HVAC) cooling with time shift of workloads within individual data centers (DC). This paper introduces Green-DCC, which proposes a Reinforcement Learning (RL) based hierarchical controller to optimize both workload and liquid cooling dynamically in a DCC. By incorporating factors such as weather, carbon intensity, and resource availability, Green-DCC addresses realistic constraints and interdependencies. We demonstrate how the system optimizes multiple data centers synchronously, enabling the scope of digital twins, and compare the performance of various RL approaches based on carbon emissions and sustainability metrics while also offering a framework and benchmark simulation for broader ML research in sustainability.
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