An Efficient Task Scheduling Based on Hybrid Bird Swarm Flow Directional Model in Cloud Computing Environment

Abstract

The cloud computing has developed a vital service in information technology (IT). It assures resource pooling as well as offers services on-demand around the network. The efficient task scheduling and the balanced task distribution become the major challenging problem in the cloud computing system because of the active heterogeneous nature of resources as well as the tasks. The resources are unstable in nature whenever a large number of resources are requested for completing the tasks. The main part of this issue is to design the effective intelligent searching arrangement for scheduling the tasks in suitable virtual machines and how VM schedules the task in the efficient way. In this article, an effective method using MAP reduces structure and HBSFD for the effective task scheduling in the provided cloud. First, from the client’s task, task features are extracted. Later, these extracted features are chosen by the feature choice using the adjusted rand index and the standard deviation ratio (FSASR) method. Then, the larger tasks are divided to smaller subtasks using the map-reduce structure. Finally, the tasks are effectively scheduled with the help of the hybrid bird swarm-based flow directional algorithm (HBSFD). The experimental evaluations are conducted on the platform of cloudsim, and the experimental outcomes demonstrates that the proposed HBSFD technique performs better than other state-of-art approaches with respect to measures such as average turnaround time and processing time.