Prediction Model for Virtual Machine Power Consumption in Cloud Environments

AbstractPower consumption has become a crucial issue in cloud computing environments because of environmental and financial concerns. It is necessary to estimate individual virtual machine power consumption to enforce efficient power aware policies in cloud. Existing solutions are built on linear power models to infer power consumption through VM resource utilization. However, linear models do not capture dependencies among multiple parameters and hence they do not ensure prediction accuracy across multiple workloads. In this paper, a non-linear support vector regression based power model using performance monitor counters is proposed to predict individual virtual machine power consumption. Experimental results with various standard benchmark workloads demonstrate that the prediction accuracy of proposed approach is better than the existing linear regression based power model