Quality of service aware improved coati optimization algorithm for efficient task scheduling in cloud computing environment

The task scheduling has a potential impact on overall system performance and resource utilization in the domain of Cloud Computing. Cloud computing adopts a cloud service provider (CSP) for facilitating access and delivering services to the shared resources. Task scheduling in clouds can be attained some symmetry form which helps in achieving predominant resource optimization that includes energy efficiency and load balancing. This task scheduling process of cloud computing pertains to the problem of Non-deterministic Polynomial (NP) which can be significantly solved using the techniques of metaheuristic optimization for enhancing the job scheduling effectiveness. In this paper, an Improved Coati Optimization Algorithm-based Task Scheduling (ICOATS) is presented for addressing the issues of lengthier scheduling time, high consumptions of cost and maximized load on Virtual Machine (VM) in cloud computing environment. In this proposed ICOATS, a model for distribution and scheduling of tasks is constructed using the factors of VMs, cost and time. It further included a multi-objective fitness function which targets on minimizing makespan, and at the same time maximizing the rate of resource utilization. It established possible plan for every coati with respect to task scheduling process that aids in determining the best solution (optimal assignment of incoming tasks to VMs). It is proposed with the capability of handing the problem of premature convergence by incorporating an exploitation strategy which improving the local search potential with well-balanced trade-off amid exploration and exploitation. The simulation results of this ICOATS approach under its evaluation with the existing metaheuristic task scheduling approaches confirmed better improvement in reducing makespan, and simultaneously enhances the turnaround efficiency, success rate and availability.