Bridging Server and Cooling: Toward Effective Energy Management in Data Centers

Biyu Zhou, Xiaohui Song, Xiang Shi, Yijun Lu, Songlin Hu

Abstract: This paper studies the energy management problem in data centers. As the most energy-consuming sub-systems, server and cooling have been considered as the key of energy saving for data center. However, due to the fact that servers are not directly connected to cooling devices, it is not trivial to optimize them jointly. In this paper, we formulate the joint server and cooling energy management problem and analyze its challenges, and design a Learning-based Energy management Scheme (LEES) to solve the joint energy management problem. Firstly, machine learning methods are investigated in the cooling power modeling and cabinet temperature modeling. Based on the models, a simple yet efficient energy management scheme is proposed to find an optimized configuration of server load distribution, server status and air supply temperature setting for the data center to reduce the total energy consumption. Extensive simulation results demonstrate that our method is able to reduce the total energy consumption of data centers significantly.