Research on Cloud Computing Task Scheduling Based on Improved Particle Swarm Optimization Algorithm

Abstract

As Internet applications and big data technology rapidly advanc, the cloud computing industry is booming. Users can now obtain the required resources from the cloud computing environment according to their personal needs. The objective of this paper is to reduce the time required for task completion. Based on the characteristics of particle swarm optimization algorithm (PSO) and the characteristics of cloud computing task scheduling problem, an improved PSO algorithm is proposed to address the challenge of scheduling cloud computing tasks. To begin with, this paper analyzes the development status of cloud computing, and deeply studies the concept, characteristics and goals of task scheduling in cloud computing. Afterwards, the theoretical underpinnings of the PSO algorithm are delved into, and its distinguishing characteristics are fully explored: it has the advantages of fast convergence and simple calculation, but it may fall into local optimal solution. In this paper, a new inertia weight optimization scheme utilizing a sine-based approach is introduced. After analyzing the characteristics of cloud computing task scheduling, the PSO algorithm is discretized for task scheduling. The simulation results show that the improved discrete particle swarm scheduling algorithm (IDPSO) is superior to the standard discrete PSO algorithm, which reduces the time of task scheduling and improves the scheduling efficiency.