Cloud Computing—Effect of Evolutionary Algorithm on Load Balancing

Abstract

In cloud computing due to the multi-tenancy of the resources, there is an essential need for effective load management to ensure an efficient load sharing. Depends on the structure of the tasks, different algorithms could be applied to distribute the load. Workflow scheduling as one of those load distribution algorithms, is specifically designed to schedule the dependent tasks on available resources. Considering a job as an elastic network of dependent tasks, this paper describes how evolutionary algorithm, with its mathematical apparatus, could be applied as workflow scheduling in cloud computing. In this research, the impact of Generalized Spring Tensor Model on workflow load balancing, in context of mathematical patterns have been studied. This research can establish patterns in cloud computing which can be applied in designing the heuristic workflow load balancing algorithms to identify the load patterns of the cloud network. Furthermore, the outcome of this research can help the end users to recognize the threats of tasks failure in processing the e-business and e-since data in cloud environment.