IPSO: Improved Particle Swarm Optimization based Task Scheduling at the Cloud Data Center

Today, cloud computing has become an advanced form of distributed computing, grid computing, utility computing, and virtualiz anon. Efficient task scheduling algorithms help to reduce the number of virtual machines used, thus reducing costs and improving stability. To solve the problem of cloud computing task scheduling, an improved particle swarm optimization (IPSO) task scheduling method is proposed based on the traditional PSO algorithm. Firstly, this paper describes the mathematical model of cloud computing task scheduling and the basic principle of particle swarm optimization. On this basis, the random method is used to generate the initial population definition appropriateness function, the indirect coding method is used to encode the resources, and the time-varying method is used to adjust the inertia weight. In the position update, according to the inertia weight w, the individual optimal value Pbest or the group optimal value Gbest is legalized to determine the update method of the particle velocity and position, thereby increasing the degree of discretization of the PSO algorithm. The simulation test on the CloudSim platform shows that the scheduling strategy is effective and efficient. Experimental results demonstrate that the proposed method obtains better scheduling results. Thereby controlling global search and local search, try to avoid falling into local optimum.