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.

本研究提出一种基于多智能体强化学习和演员 - 评论家方法的算法，通过与真实工作负载模式、能源价格和碳强度相互作用的云系统学习最优的协同调度策略，从而最大化 GPU 利用率、降低运营成本和减少碳排放。与其他算法相比，我们的方法提高了系统效用，最高可达 28.6％。