Optimizing Task Scheduling in Cloud Computing Environments using Hybrid Swarm Optimization

Abstract

Cloud computing has revolutionized the Information Technology (IT) landscape by offering on-demand access to a shared pool of computing resources over the internet. Effective task scheduling is pivotal in optimizing resource utilization and enhancing the overall performance of cloud systems. Tasks are allocated to virtual machines (VMs) based on a server's workload capacity, aiming to minimize traffic congestion and waiting times. Although Particle Swarm Optimization (PSO) is currently the most effective algorithm for task scheduling in cloud environments, this study introduces a Hybrid Swarm Optimization (HSO) algorithm that combines the strengths of PSO and Salp Swarm Optimization (SSO). The proposed hybrid algorithm addresses the challenges associated with task scheduling in cloud computing. The performance of the HSO algorithm is evaluated using the CloudSim simulator and compared against traditional scheduling algorithms. Simulation results indicate that the hybrid PSO-SSO algorithm outperforms existing methods regarding makespan time, cloud throughput, and task execution efficiency. Consequently, the hybrid approach significantly improves resource utilization and overall system performance in cloud computing environments.