Energy-efficient resource allocation technique using flower pollination algorithm for cloud datacenters

Abstract

Cloud Computing is modernizing how Computing resources are created and disbursed over the Internet on a model of pay-per-use basis. The wider acceptance of Cloud Computing give rise to the formation of datacenters. Presently these datacenters consumed a lot of energy due to high demand of resources by users and inefficient resource allocation technique. Therefore, resource allocation technique that is energy-efficient are needed to minimize datacenters energy consumption. This paper proposes Energy-Efficient Flower Pollination Algorithm (EE-FPA) for optimal resource allocation of datacenter Virtual Machines (VMs) and also resource under-utilization. We presented the system framework that supports allocation of multiple VMs instances on a Physical Machine (PM) known as a server which has the potential to increase the energy efficiency as well resource utilization in Cloud datacenter. The proposed technique uses Processor, Storage and Memory as major resource component of PM to allocate a set of VMs, such that the capacity of PM will satisfy the resource requirement of all VMs operating on it. The experiment was conducted on Multi-RecCloudSim using Planet workload. The results indicate that the proposed technique energy consumption outperform the benchmarking techniques which include GAPA, and OEMACS with 91% and 94.5% energy consumption while EE-FPA is around 65%. On average 35% of energy has been saved using EE-FPA and resource utilization has been improved.