Robust Virtual Machine Consolidation for Efficient Energy and Performance in Virtualized Data Centers

Abstract

Cloud providers use virtualization technologies to provide an isolated execution environment and agile resource provisioning. However, virtualized data centers consume huge amounts of energy, which increases the operational costs. To optimize resource usage and reduce energy consumption of Infrastructure as a Service (IaaS) Cloud, it needs a continuous monitoring and consolidation of VMs using live migration and switching idle hosts to the sleep state. In this paper, we propose a robust consolidation approach to achieve equilibrium between energy and performance. The proposed approach consists of three algorithms: over-utilized host detection, VM selection, and VM placement. Additionally, we implement an adaptive historical window selection algorithm for reducing ineffective VM migration. To validate our approach, we implemented it using Cloud Sim simulator and conducted simulations for different days of a real workload trace of Planet Lab. The results show that our approach reduced the number of power change, the number of migrations, and average SLA violations by 38%, 74.8%, and 31.8%, respectively. Furthermore, it can decrease the energy consumption of network that results from VM migration.