Efficient Virtual Machine Placement with Energy Saving in Cloud Data Center

Abstract

Cloud data centers provide computing infrastructure as a service to their customers on pay per use basis. In virtualized data centers CPU, RAM, storage and bandwidth are allotted to a Virtual Machine (VM) from pool of shared resources. An autonomic consolidation of VMs on appropriate Physical Machine (PM) by achieving performance and saving cost is the key challenge for virtualized data centers. This paper presents a self-organizing and multi-objective approach for autonomic consolidation of VMs. The proposed approach does the initial placement of VMs in appropriate PM of cloud data centre which addresses different issues altogether such as maximum resource requirement during setup of VMs, future demand of free resources at peak load, improving the performance and energy saving by keeping idle PMs at offline state. The performance of the proposed algorithm is evaluated by simulating a data center with randomly generated resource capacities of PMs and resource requirement of VMs. Experiment results of proposed technique are also compared with standard algorithms of VM consolidation such as first-first, next-fit and random-selection on two dimensions of resources-CPU and RAM.