Energy-Efficient and Load-Aware VM Placement in Cloud Data Centers

Abstract

VM consolidation has been proposed as an effective solution to improve resource utilization and energy efficiency through VM migration. Improper VM placement during consolidation may cause frequent VM migrations and constant on–off switching of PMs, which can significantly hurt QoS and increase energy consumption. Most existing algorithms on efficient VM placement suffer the problem of easily falling into a sub-optimum prematurely since they are heuristic. Also, they do not achieve a good balance between multiple different goals, such as resource utilization, QoS, and energy efficiency. To address this problem, we propose an effective and efficient VM placement approach called MOEA/D-based VM placement, with the goal of optimizing energy efficiency and resource utilization. We develop an improved MOEA/D algorithm to search for a Pareto-compromise solution for VM placement. Our experiment results demonstrate that the proposed multi-objective optimization (MOO) model and VM placement solution have immense potential as it offers significant cost savings and a significant improvement in energy efficiency and resource utilization under dynamic workload scenarios.