Jointly Optimizing the IT and Cooling Systems for Data Center Energy Efficiency based on Multi-Agent Deep Reinforcement Learning
Ce Chi, Kaixuan Ji, Avinab Marahatta, Penglei Song, Fa Zhang, Zhiyong Liu
Abstract
With the development and application of cloud computing, the increasing amount of data centers has resulted in huge energy consumption and severe environmental problems. Improving the energy efficiency of data centers has become a necessity. In this paper, in order to improve the energy efficiency of both IT and cooling systems for data centers, a model-free deep reinforcement learning (DRL) based joint optimization approach MACEEC is proposed. To improve the cooperation between IT and cooling system while handling the high-dimensional state space and the large hybrid discrete-continuous action space, a hybrid AC-DDPG multi-agent structure is developed. A scheduling baseline comparison method is proposed to enhance the stability of the architecture. And an asynchronous control optimization algorithm is developed to solve the different responding time issue between IT and cooling system. Experiments based on real-world traces data validate that MACEEC can effectively improve the overall energy efficiency for data centers while ensuring the temperature constraint and service quality compared with existing joint optimization approaches.