Comparative analysis of Scheduling algorithms with Load balancing in Cloud Computing

Abstract

The evolution of cloud computing over the last few years has been one of the biggest developments in the history of computing. Cloud computing is separate from grid computing and distributed computing. Google Apps, supported by Google and Microsoft SharePoint, is an example of cloud computing that allows users to access services through a browser and is installed on millions of machines across the Internet. This paper discusses the comparative analysis of scheduling algorithms using credits and load balancing named as CBSA-LB and Enhanced multi objective comprises with load balancing algorithm termed as EMOSA-LB. The load balancing strategy used is bee colony optimization algorithm for both the scheduling algorithms. The basic motive behind this paper is to evaluate a steady distribution of workload on computational capabilities of VMs. The outcomes revealed that CBSA_LB provides substantial improvement against EMOSA_LB regarding makespan time, throughput and total execution time.