Cloud Computing Task Scheduling based on Improved Particle Swarm Algorithm

To further improve the efficiency and performance of cloud computing task scheduling and cope with the diversity of user tasks, the study unfolds the construction of cloud computing task scheduling model based on heuristic intelligent optimization algorithm based on artificial intelligence technology. The results show that the improved optimization algorithm designed by the study performs well on the hypervolume and anti-generation distance, obtaining a maximum value of 0.976 and a minimum value of 0.136, respectively, and has a good ability to find the optimal solution. The scheduling model has strong convergence performance, and the completion time is 17.64s, 39.71s, 61.69s and 86.23s when the number of tasks is 60, 120, 180 and 240, respectively, with high scheduling efficiency. Meanwhile, the highest value of resource utilization can reach 95.36%, and the load balancing performance is good. The design of the study can achieve reasonable resource allocation and task scheduling, which helps to improve the overall resource utilization of the data center.