A Genetic Based Improved Load Balanced Min-Min Task Scheduling Algorithm for Load Balancing in Cloud Computing

Abstract

Cloud computing is evolving as a new model of big-scale distributed computing. It provides own services to online on-demand and pay-as-you-go basis. In cloud computing environment load balancing is a key issue which is required to distributing the dynamic workload over multiple machines to make certain that no single machine is overloaded. In order it helps in ideal use of resource and as a consequence enhancing the performance of the system, we need an efficient task scheduling algorithm. The Min-Min algorithm is simple that produce a schedule that minimize the makespan but this algorithm does not make use of resource effectively. In this paper, we proposed an Improved Load Balanced Min-Min (ILBMM) algorithm using genetic algorithm (GA) in order to minimize the makespan and increase the utilization of resource. The implementation of proposed algorithm has been completed using CloudSim simulator and simulation outcomes demonstration that the proposed algorithm outperforms to current algorithm on same objectives.