Orthogonal Grey Wolf Optimization Algorithm for Task Scheduling in Cloud Environment

Cloud Computing (CC) is being popularly used by small organizations and startups as a business model in distributed computing environment. In cloud computing, it provides three architectures SaaS, the user tasks are organized and executed with suitable resources PaaS and IaaS to deliver the different types of services to the customer. In cloud, the time complexity of task execution is a common problem. In this paper, an efficient Task Scheduling (TS) technique is proposed namely Orthogonal Grey Wolf Optimization (O-GWO) algorithm. The O-GWO algorithm, schedules the tasks on VMs with minimum execution time. Also, it increases the convergence speed and reduces the Degree of Imbalance (DI) of total task schedule across VMs. An experimental analysis shows that, proposed O-GWO algorithm performance is measured with respect to efficient evaluation metrics such as makespan and Degree of Imbalance. The makespan of proposed O-GWO algorithm is achieved to the extent of 38.20 seconds of enhancement compared to the existing Hybrid Particle Swarm Optimization with Simulated Annealing (HPSO-SA).