Optimizing Power Consumption in Cloud Computing based on Optimization and Predictive Analysis

Due to the budget and the environmental issues, achieving energy efficiency gradually receives a lot of attentions these days. In our previous research, a prediction technique has been developed to improve the monitoring statistics. In this research, by adopting the predictive monitoring information, our new proposal can perform the optimization to solve the energy issue of cloud computing. Actually, the optimization technique, which is convex optimization, is coupled with the proposed prediction method to produce a near-optimal set of hosting physical machines. After that, a corresponding migrating instruction can be created eventually. Based on this instruction, the cloud orchestrator can suitably relocate virtual machines to a designed subset of infrastructure. Subsequently, the idle physical servers can be turned off in an appropriate manner to save the power as well as maintain the system performance. For the purpose of evaluation, an experiment is conducted based on 29-day period of Google traces. By utilizing this evaluation, the proposed approach shows the potential to significantly reduce the power consumption without affecting the quality of services.