Method for evaluation on energy consumption of cloud computing data center based on deep reinforcement learning

Abstract

In view of the problem that the current method for evaluation on energy consumption of cloud computing data center has larger fluctuations of relative error and deviation value for evaluated power variation and greater evaluation training losses, this paper proposes an energy consumption evaluation method for cloud computing data center. Firstly, we build an energy consumption evaluation model, analyze the correlation between independent variables and dependent variables through multiple linear regression model and multiple nonlinear regression model, and then measure the accuracy of the evaluation results by using average relative deviation and relative deviation; The depth learning mechanism is introduced, and the depth residual network is used for iterative training of regression parameters to complete the mapping of regression parameters; Comprehensively obtain the regression parameters in the energy consumption model, put them into the memory playback pool, solve the energy consumption evaluation function according to the sub strategy parameters, and reconstruct the energy consumption model of cloud computing data center. The comprehensive evaluation of energy consumption of cloud computing data center is realized. The experimental results show that the energy consumption deviation of this method is small and stable. It can effectively reduce the relative error of evaluation power change and reduce the evaluation training loss.