An Efficient Task Scheduling in a Cloud Computing Environment using Hybrid Swarm Optimization

Abstract

Information technology (IT) services are available on demand over the Internet under the current computing standard known as cloud computing. A work is mapped with an available resource in the cloud environment to get a good result. Cloud computing has emerged as a transformative technology that provides on-demand access to a shared pool of computing resources over the internet. Effective task scheduling plays a crucial role in optimizing resource utilization and improving overall system performance in cloud environments. According to a server's workload capacity, tasks are distributed across its virtual machines (VMs) via task scheduling. The server's workload is planned out to reduce traffic and wait times. The best algorithm currently in use to schedule a work to an existing resource in a cloud setting is particle swarm optimization (PSO). PSO schedules the task for an available resource to cut down on computational costs. In order to address job scheduling challenges in the cloud environment, a hybrid swarm optimizations (HSO) algorithm—a combination of PSO and salp swarm optimization (SSO)—is suggested in this paper. The hybrid algorithm leverages the strengths of both PSO and SSO to achieve better optimization results. The proposed method is evaluated using the CloudSim simulator and compared with traditional scheduling algorithms. Simulation results demonstrate the superiority of the hybrid PSO-SSO algorithm in terms of makespan time, cloud throughput, and task execution efficiency. The findings suggest that the hybrid approach can significantly enhance resource utilization and performance in cloud computing environments.