An enriched chaotic quantum whale optimization algorithm based job scheduling in cloud computing environment

Abstract

In recent years, cloud has become a metaphor for voluminous data storage and utilization of virtual resources by cloud user. This study focuses on independent job scheduling in cloud computing paradigm. This paper devised a new enriched approach of chaotic quantum whale optimization algorithm (CQWOA), whose ultimate objective is to overwhelm degree of imbalance, increasing makespan and overheads in cost, energy consumption, resource utilization. With the intelligence of chaotic mapping and the quantum mechanism based optimal virtual machine selection, the global optima is achieved more significantly by CQWOA. This algorithm discovers best location and the direction to detect appropriate virtual machine in terms of reduced resource utilization, increasing makespan and evenly distributing the work load, makes the presented model to be more superior than Particle swarm optimization, Ant colony Optimization and standard Whale optimization. The existing models fails to handle the inconsistencies and vagueness in discovering potential virtual machine’s which qualifies their requirements and standard whale optimization easily meets earlier converge of local optima and it is very complex for them to reach global best virtual machines in cloud computing Paradigm. The proposed CQWOA model has saved the total execution cost in job scheduling more successfully and it is proved by its simulation results.