Multi-Objective Low-Carbon Scheduling Method for Data Centers Based on Ensemble Reinforcement Learning

The explosive growth of GPU data centers is driven by the surging demand for machine learning, artificial intelligence, and high-performance computing applications, with the pressure on the power grid intensifying. Meanwhile, as renewable energy becomes more integrated into the grid, the carbon intensity of grid electricity varies at different times. This variability presents an opportunity for data centers to align their workloads with periods of high renewable energy generation and low carbon intensity, thereby reducing their operational carbon emissions. This paper proposes a multi-objective scheduling strategy to minimize carbon emissions and maximize quality of service (QoS) by identifying the Pareto front. The proposed strategy employs an integrated reinforcement learning model to dynamically integrate action strategies for multiple objectives. Results from a case study based on a real-world, large-scale data center illustrate the method’s effectiveness. This effectiveness arises from its dual-objective job scheduling strategy, which simultaneously reduces carbon emissions from electricity consumption and maintains a high quality of service in data centers. The method aims to promote the low-carbon operation of data centers, provide decision support, and facilitate coordination between data centers and power systems in real-world applications.