Energy-Efficient Task Distribution Using Neural Network Temperature Prediction in a Data Center

Abstract

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 temperature-based 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 non-predictive approach. The results indicated that the proposed method can reduce future CPU temperatures successfully compared to the non-predictive approach, though in exchange for high computational cost.