Power and Resource-Aware VM Placement in Cloud Environment

Abstract

Cloud computing provides various services to the cloud consumers based on demand and pay per use basis. To improve the system performance (such as energy efficiency, resource utilization (RU), etc.) more than one virtual machine (VM) can be deployed on a server. Efficient VM placement policy increases the system performance by utilizing all the computing resources at their maximum threshold limit and reduce the probability to become a server overloaded/underloaded. Overloaded/underloaded servers consume more energy and increase the number of VM migration in comparison to the server which is in a normal state. In this paper, Energy and Resource-Aware VM Placement (ERAP) algorithm is presented. This algorithm considers both, energy as well as central processing unit (CPU) utilization to deploy the VMs on the servers. CloudSim toolkit is used to analyze the behavior of the ERAP algorithm. The effectiveness of the ERAP algorithm is tested on real workload traces of Planet Lab. Results show that ERAP algorithm performs better in comparison to the existing algorithm on the account of the number of VM migrations, total energy consumption, number of servers shutdowns, and average service level agreement (SLA) violation rate. Results show that on average 13.12% energy consumption is minimized in contrast to the existing algorithm.