A Task Scheduling Algorithm Based on Improved Particle Swarm Optimization for Cloud Computing

Facing with large amount of tasks in cloud computing, a task scheduling algorithm based on improved Particle Swarm Optimization(PSO) was taken into research to minimize the task completion time and maximize the resource utilization. Firstly, the velocity and position of each particle are set randomly within the search space in the initialization. A swarm of particles are used to represent the potential solutions and the position of each particle is encoded by natural number. After each iteration update, legalized method was used to repair the particle. In order to reduce the probability of particles running out of the solution space, some method was taken to limit the velocity of each particle. For overcoming precocious, chaos was combined with PSO. By using chaos disturbance, the particles can have a better position. Cloudsim was used to stimulate cloud computing environment for experimental test. Experimental results show that compared with tradition PSO the improved PSO converges faster and have a better scheduling result.