Recent breakthroughs in generative artificial intelligence have triggered a surge in demand for machine learning training, which poses significant cost burdens and environmental challenges due to its substantial energy consumption. Scheduling training jobs among geographically distributed cloud data centers unveils the opportunity to optimize the usage of computing capacity powered by inexpensive and low-carbon energy and address the issue of workload imbalance. To tackle the challenge of multi-objective scheduling, i.e., maximizing GPU utilization while reducing operational costs, we propose an algorithm based on multi-agent reinforcement learning and actor-critic methods to learn the optimal collaborative scheduling strategy through interacting with a cloud system built with real-life workload patterns, energy prices, and carbon intensities. Compared with other algorithms, our proposed method improves the system utility by up to 28.6% attributable to higher GPU utilization, lower energy cost, and less carbon emission.

翻译：近年来生成式人工智能的突破性进展引发了机器学习训练需求的激增，由于其巨大的能源消耗，这带来了显著的成本负担和环境挑战。在分布式云数据中心之间调度训练任务，为优化利用廉价低碳能源驱动的计算能力并解决工作负载不均衡问题提供了机遇。针对多目标调度挑战（即在最大化GPU利用率的同时降低运营成本），我们提出了一种基于多智能体强化学习与演员-评论家方法的算法，通过与基于真实工作负载模式、能源价格和碳强度构建的云系统交互，学习最优协作调度策略。与其他算法相比，我们的方法通过提高GPU利用率、降低能源成本和碳排放，使系统效用提升最高达28.6%。