Energy-aware fruitfly optimisation algorithm for load balancing in cloud computing environments

Abstract

An effective task scheduling is one of the vital aspects for effectually hitching the potential of cloud computing. The most important aspect of task scheduling focuses on balancing the load of tasks among virtual machines, which is independent in nature. Energy conservation is one of the major key issues in cloud environment which in turn reduces operation costs in cloud datacenter. Meanwhile, Energy-aware load balancing optimisation technique is a promising way to attain the goal. To ensure fast processing time and optimum utilization of the cloud resources, we propose an energy-aware Fruit fly optimisation algorithm (EFOA-LB) for balancing the load among virtual machines in the cloud system. The energy-aware EFOA-LB is a modern swarm intelligence algorithm inspired by the foraging behavior of fruit flies, aims to attain well-balanced load on virtual machines and reduces energy consumption accordingly. Based on results obtained from our simulations, the proposed algorithms minimizes makespan and reduces the energy consumption of the datacenter, while meeting the task performance. The experiment results indicate that the energy-aware EFOA-LB algorithm is more efficient than the existing load balancing algorithms.