Load balancing in Cloud Computing Environment using Modified Genetic Algorithm

Abstract

Cloud Computing has brought significant changes in the field of computing. Load balancing is a method by which the distribution of dynamic workload to various virtual machine in a host can be done so that particular host or machine will not be overloaded while other being ideal. Cloud Load Balancing has always been the crucial area to work and to come up with an approach that also has to take care of the constraints such as processing power, limited storage, bandwidth, etc. The aim of this work is to reduce in total response time and total execution time with the use of modified Genetic Algorithm. The method proposed will effectively schedule the cloudlets of the particular Virtual Machine with less loaded host in datacenter. The proposed method is implemented in the Cloudsim simulator and outperformed many static existing algorithms like First Come First Serve (FCFS), Round Robin (RR), Shortest Job First (SJF) and existing traditional Genetic Algorithm.