Virtual Machine Consolidation with Usage Prediction for Energy-Efficient Cloud Data Centers

Abstract

Virtual machine consolidation aims at reducing the number of active physical servers in a data center, with the goal to reduce the total power consumption. In this context, most of the existing solutions rely on aggressive virtual machine migration, thus resulting in unnecessary overhead and energy wastage. This article presents a virtual machine consolidation algorithm with usage prediction (VMCUP) for improving the energy efficiency of cloud data centers. Our algorithm is executed during the virtual machine consolidation process to estimate the short-term future CPU utilization based on the local history of the considered servers. The joint use of current and predicted CPU utilization metrics allows a reliable characterization of overloaded and under loaded servers, thereby reducing both the load and the power consumption after consolidation. We evaluate our proposed solution through simulations on real workloads from the Planet Lab and the Google Cluster Data datasets. In comparison with the state of the art, the obtained results show that consolidation with usage prediction reduces the total migrations and the power consumption of the servers while complying with the service level agreement.