Task Scheduling Based On Ensemble Optimization Technique in Cloud Computing Environment

Abstract

Cloud computingis a trend knowledge that enables users in their pay-per-use model to use computing services remotely. Task Scheduling is a repetitive task in order for both consumers and service providers to offer their services on demand, with a quality of service (QoS) provision. Therefore, the above mentioned problem inspired us to develop the ensemble optimization techniques (EOT) such as Improved Multi-Verse Optimizer (IMVO) and Binary Salp Swarm algorithm (BSSA) for task scheduling in cloud environments. In this paper, the IMVO is improved by calculating the average waiting time as fitness function for finding the optimal solution.The proposed Salp Swarm algorithm used to optimize and mapping the cloud computing application by the design of salp in the quest and sea food navigation. Simulations are conducted with other popular metaheuristic algorithms to evaluate the efficacy of EOTin terms of resource utilization, makespan, throughput, and average waiting time.