Energy-Efficient virtual machine allocation technique using interior search algorithm for cloud datacenter

Abstract

Cloud Computing is revolutionizing how Computing power is generated and consumed over the Internet on a pay-peruse basis over the past few years. The broader acceptance of Cloud technologies has led to the establishment of datacenters. Over the years, high energy consumption by datacenters has become a major interest as a result of increasing demands of resources and services by enterprise and scientific applications. Consequently, datacenter infrastructure turns out to be not only expensive to sustain, but also unfavorable to the surrounding environment due to their huge carbon emission. Thus, energy efficient virtual machine allocation techniques are required to overcome high energy consumption due to improper resource allocation within the data centers. This paper proposes Energy-Efficient Virtual Machine allocation technique using Interior Search Algorithm (ISA) that reduces the datacenter energy consumption and resource underutilization. The results shows that, the energy consumption of GA and BFD is 90%–95% as compare to the proposed EE-IS which around 65%. On average 30% of energy has been save using EE-IS as well the utilization of the resources which has also improved.