Bridging Server and Cooling: Toward Effective Energy Management in Data Centers This paper studies the energy management problem in data centers. As the most energyconsuming subsystems, 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 Learningbased 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.

EnergyEfficient Task Distribution Using Neural Network Temperature Prediction in a Data Center The growing demand for computing resources leads to a serious problem of excessive energy consumption in data centers. In recent studies, energy consumption of both computing and cooling equipment is drawing attention. For improving the energy efficiency of cooling equipment such as computer room air conditioners (CRACs), it is neccesary to predict temperatures in data centers and to optimize thermal management in data centers. In this study, we propose a temperature prediction method for servers in a data center using a neural network. We used the prediction result for distributing task targeting temperaturebased load balancing. First, we conducted an experiment in a real data center to evaluate the prediction accuracy of the proposed method. We then simulated task distribution based on the predicted temperatures and compared the maximum CPU temperature with a nonpredictive approach. The results indicated that the proposed method can reduce future CPU temperatures successfully compared to the nonpredictive approach, though in exchange for high computational cost.