Research on Cloud Computing Task Scheduling Based on PSOMC

How to better reduce the task scheduling time and consumption cost in cloud computing has always been a hot topic of current research. In this paper, we propose a cloud computing task scheduling strategy based on the fusion of Particle Swarm Optimization and Membrane Computing. Firstly, a task scheduling model with time function and cost function as the target is proposed, secondly, on the basis of particle swarm algorithm, chaos operation is used in population initialization to improve the diversity of rich understanding, adaptive weight factor based on sinusoidal function is used to avoid the algorithm falling into local optimum, Membrane Computing is used in individual screening to improve the quality of individual solutions, and finally, in The performance of the PSOMC algorithm is illustrated by comparing six benchmark test functions in simulation experiments, and it is also verified that the completion time and consumption cost are significantly better than those of the ACO, PSO and MC algorithms for different number of tasks.