Research On Cloud Computing Task Scheduling Based On PSO Improved Algorithm

The task scheduling problem is a hot issue in the field of cloud computing.The essence of the problem is how to distribute the tasks submitted by the users to the resources of the cloud data center to achieve efficient use and balanced distribution of system resources.Particle swarm optimization algorithm has obvious advantages in solving problems such as task scheduling and combinatorial optimization,so it has become the preferred algorithm for us to study task scheduling problems in cloud computing.However,the solution obtained by the particle swarm optimization algorithm is sensitive to the quality of the initial population,and the particle swarm optimization algorithm is easy to obtain only the local optimal solution because the convergence speed is too fast.Therefore,this paper proposes an improved PSO algorithm(OBL-TP-PSO)that incorporates opposition-based learning and trial heuristics.The algorithm uses the opposition-based learning method to generate the initial population and improves the quality of the initial population.At the same time,using the trial heuristics search strategy to search the solution's solution space not only improves the local search ability of the particle,but also improves the global search ability of the particle,making the algorithm easier to obtain the global optimal solution.Finally,the simulation experiments of task scheduling in cloud environment are carried out on Min-Min algorithm,Max-Min algorithm,PSO algorithm and OBL-TP-PSO algorithm on CloudSim platform.The results show that the convergence speed of OBL-TP-PSO algorithm is faster than that of PSO algorithm;the total execution time of the task corresponding to the solution obtained by OBL-TP-PSO algorithm is Min-Min algorithm,Max-Min algorithm and PSO The total execution time of the task corresponding to the solution obtained by the algorithm is small;in terms of system load balancing,the OBL-TP-PSO algorithm also has better performance.It can be seen that the improved PSO algorithm can obtain a better task scheduling scheme and effectively improve the overall efficiency of the task scheduling algorithm in the cloud environment.