Energy Aware Cluster Based Optimal Virtual Machine Placement in Cloud Environment

Abstract

A good virtual machine placement strategy would make better consolidation of virtual machines in a data center that optimizes resource utilization and hence reduces energy consumption. However, it is hard to balance the hosts in DCs due to the workload fluctuation by application and scaling of virtual machines (VMs). The optimal decision on VMs placement and consolidation is the NP-hard problem and many researchers proposed a solution to tackle this problem and lacks in exploiting the mechanisms efficiently. Therefore, this paper proposes a hierarchical cluster-aware approach based on a meta-heuristic crow search algorithm for the optimal selection of hosts to place the VMs and consolidates a maximum number of VMs on a minimum number of hosts. The proposed approach optimizes the energy consumption of data centers while satisfying the QoS requirements of applications to meet SLA. Evaluation of the proposed approach is performed under the cloudSim framework using real workload traces and is compared with classical approaches. Results of the experiment show that proposed work reduces energy consumption and SLA violations while ensuring better resource utilization.