A Reinforcement Learning-Based Power Management Framework for Green Computing Data Centers

Various power management techniques have been exploited to reduce the energy consumption of data centers. In this work, we propose a reinforcement learning-based power management framework for data centers, which does not rely on any given stationary assumptions of the job arrival and job service processes. By carefully designing the state space, the action space, and the reward of a learning process, the objective of the reinforcement learning agent coincides with our goal of reducing the server pool energy consumption with reasonable average job response time. Real Google cluster data traces are used to verify the effectiveness of the proposed reinforcement learning-based data center power management framework.