Gravitational Emulation-Grey Wolf Optimization technique for Load balancing in Cloud Computing

Abstract

Cloud computing is a virtual pool of computing resources such as software, platform, infrastructures, applications, storage and information provides to users through the internet. The computing capacity of the virtualized cloud frame is divided into several VMs with the help of virtualization methodology. In the cloud environment, load balancing is the challenging task because distribute the task equally among the VMs is a complex one. In this paper, hybrid Grey-Wolf-Optimization (GWO) algorithm and Gravitational-Emulation-Local-Search (GELS) algorithm is utilized for cloud load balancing. A GWO based optimization method is used for ranking the host resources based on their efficiency and utilization. The GELS algorithm is used for clustering the tasks based on their VM ability and cost. The proposed GWO-GELS method significantly decrease the response time and cost. An experimental analysis is demonstrated that the makespan performance of proposed method achieved 10msec lesser than existing methods. The evaluated result validated that GWO-GELS performed effectively by means of energy consumption minimization, reduction of cost, makespan and avoid Degree of Imbalance (DI). The GWO-GELS method is implemented in CloudSim.