As machine learning workloads significantly increase energy consumption, sustainable data centers with low carbon emissions are becoming a top priority for governments and corporations worldwide. This requires a paradigm shift in optimizing power consumption in cooling and IT loads, shifting flexible loads based on the availability of renewable energy in the power grid, and leveraging battery storage from the uninterrupted power supply in data centers, using collaborative agents. The complex association between these optimization strategies and their dependencies on variable external factors like weather and the power grid carbon intensity makes this a hard problem. Currently, a real-time controller to optimize all these goals simultaneously in a dynamic real-world setting is lacking. We propose a Data Center Carbon Footprint Reduction (DC-CFR) multi-agent Reinforcement Learning (MARL) framework that optimizes data centers for the multiple objectives of carbon footprint reduction, energy consumption, and energy cost. The results show that the DC-CFR MARL agents effectively resolved the complex interdependencies in optimizing cooling, load shifting, and energy storage in real-time for various locations under real-world dynamic weather and grid carbon intensity conditions. DC-CFR significantly outperformed the industry standard ASHRAE controller with a considerable reduction in carbon emissions (14.5%), energy usage (14.4%), and energy cost (13.7%) when evaluated over one year across multiple geographical regions.

翻译：随着机器学习工作负载显著增加能源消耗，低碳排放的可持续数据中心正成为全球政府和企业最优先关注的事项。这要求优化冷却与IT负载的功耗、基于电网可再生能源可用性灵活调整负载、以及通过协作代理利用数据中心不间断电源的电池储能，实现范式转变。这些优化策略与天气、电网碳强度等可变外部因素之间的复杂关联使得该问题极具挑战性。目前尚缺乏能在动态现实环境中同时优化所有目标的实时控制器。我们提出一种数据中心碳足迹削减（DC-CFR）多智能体强化学习（MARL）框架，该框架针对碳足迹削减、能源消耗和能源成本等多个目标优化数据中心。结果表明，DC-CFR MARL智能体有效解决了实时优化冷却、负载转移与储能之间复杂的相互依赖关系，可适用于不同地理位置下真实动态天气与电网碳强度条件。在跨多个地理区域的全年评估中，DC-CFR显著优于行业标准ASHRAE控制器，实现碳排放（14.5%）、能源使用（14.4%）和能源成本（13.7%）的大幅削减。