Abstract: With the continuous expansion of the scale of data centers,the problems of high energy consumption,high operating costs and environmental pollution are becoming more and more serious,which seriously affect the sustainability of data centers.Most data center energy consumption optimization methods focus tasks on as few servers as possible,so as to reduce computing energy consumption.However,this often leads to the generation of data center hotspots and increases cooling energy consumption.In order to solve this problem,this paper first models the data center,and models the total energy consumption optimization problem of the data center as a task scheduling problem,and requires that no data center hotspots are generated.This paper proposes a task scheduling method based on deep reinforcement learning for data centers,and uses reward shaping to optimize the method to reduce the total energy consumption of data centers without generating hotspots.Finally,experiments are carried out through simulation environment and real data center load trace data.The simulation results show that the proposed method can reduce the total energy consumption of the data center better than other existing scheduling methods,and can reduce the total energy consumption by up to 25.5%.In addition,the proposed optimization method does not generate hot spots yet,which further proves its superiority.

Key words: Data Center, Energy consumption optimization, Hot spot, Task scheduling, Deep reinforcement learning, Reward shaping