Multi-objective Task Scheduling in Cloud Computing Environment by Hybridized Bat Algorithm

Abstract

Cloud computing is a relatively new computing technology, which provides online on-demand computing services to cloud users. Task scheduling plays a crucial role in the cloud model. An efficient task allocation method, results with better resource utilization, have an impact on the quality of service, the overall performance, and user experience. The task scheduling should be carried out on multiple criteria, which is a difficult optimization problem and belongs to the class of NP-hard optimization problem. As the complexity of the problem increases, the exhaustive search becomes enormous. Consequently, an optimization technique is needed that can find the approximate solution in less amount of time. In this paper, we propose a hybridized bat optimization algorithm for multi-objective task scheduling. The simulations are performed in the CloudSim toolkit using standard parallel workloads, and the obtained results show that the proposed technique gives better results than other similar methods.