Research On The Application Of Swarm Intelligence Algorithm For Cloud Computing Task Scheduling

With the advent of the Industry 4.0 era,smart manufacturing and the interconnection of everything gradually become the theme of the machinery industry system,the traditional information processing model can not meet the needs of the industrial system of massive data and largescale information processing,and the emerging cloud computing model can not only handle massive data flow,but also coordinate information from various places to achieve the requirements of information interconnection,which can provide data storage and processing,resource elasticity and flexibility,data analysis and prediction,virtualization and simulation,as well as cloud collaboration and remote access to support and help promote the digital transformation and business optimization of the machinery industry.And cloud computing resources such as storage,applications and other services need to be scheduled for optimal usage of these services.Therefore,task scheduling is crucial to achieve accuracy and correctness of task completion.This paper focuses on the optimization of the cloud computing task scheduling problem using swarm intelligence algorithms,and the main research is as follows:(1)In this paper,an improved Whale Optimization Swarm Intelligence Algorithm(IWOA)is proposed,aiming to optimize the task completion time of cloud computing task scheduling.After adjusting the exploration ability and exploitation ability of each period in the algorithm,improving the linear convergence factor on the basis of the Whale Optimization Algorithm and introducing the variation strategy in the differential evolution algorithm,the improved Whale Optimization Algorithm has better performance in cloud computing task completion time and load balancing compared with the traditional Whale Optimization Algorithm and the basic particle swarm algorithm.(2)In this paper,we propose a hybrid gray wolf optimization for cloud computing task scheduling algorithm(Particle Swarm Optimization and Grey Wolf Optimizer,PSO_GWO)combining chaotic initialization and particle swarm algorithm with task completion time and task completion cost,etc.as optimization objectives in cloud computing task scheduling.Experimental results show that the improved algorithm converges faster than the other two algorithms,while achieving more significant reductions in both cloud computing task completion time and task completion cost,and better performance in terms of resource utilization.(3)This paper designs and implements a cloud computing task scheduling system by combining the two proposed improved algorithms.The system is designed by python language and can realize the uploading of mechanical data to the cloud computing system and the scheduling management of it at the same time,with many functions such as uploading data,scheduling data and displaying data,etc.This paper adopts the mechanical workshop data set and conducts scheduling to obtain the results,which reflects the engineering application of the paper.This paper uses the machine shop dataset and schedules the results,reflecting the engineering application of the paper.