Cloud computing task scheduling based on improved particle swarm optimization algorithm
Yuping Zhang, Rui Yang · IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society · 2017
This paper aims to propose a task scheduling algorithm based on particle swarm optimization algorithm, which can schedule efficiently, shorten the task completion time and improve the utilization of resources in cloud computing. First, the position of the particle is encoded by the natural number, and the population is initialized randomly in the solution space. Then, the particle is repaired to reduce the probability that the particle runs out of the solution space and the particle velocity is limited after each iteration. Furthermore, an improved particle swarm optimization algorithm is proposed, which is based on chaos perturbation strategy. The experimental results of Cloudsim simulation platform show that the improved Particle Swarm Optimization(PSO) has faster convergence speed and avoids premature convergence and jumps out of the local optimum.