Research On Cloud Task Scheduling Strategy Based On Improved Intelligent Optimization Algorithm

Today,with the rapid development of cloud computing technology,the combination of cloud computing and the Internet is getting closer and closer,and more and more enterprises and customers choose cloud computing services.Cloud computing is a pay-as-you-go business model.The cloud task scheduling strategy is closely related to the performance and efficiency of cloud computing.Scheduling is the focus of cloud computing research.This thesis mainly uses swarm intelligence algorithm to optimize single-objective and multi-objective cloud computing task scheduling problems.The main research contents are as follows:(1)Firstly,it introduces the development history of cloud computing and the research status of cloud task scheduling,introduces the main characteristics,service types and architecture of cloud computing,introduces the principle and optimization goal of cloud computing task scheduling,analyzes and introduces some common cloud task scheduling Algorithm and cloud simulation platform.(2)An improved whale optimization algorithm(IWOA)is proposed,which optimizes the task completion time of cloud task scheduling.In order to ensure the diversity of the population,first use the quasi-reverse learning method to initialize the population;then adjust the linear convergence factor and change it to a nonlinear convergence factor,so that the global search ability and local development ability can reach a balance between each other;finally,in order to achieve the desired convergence accuracy,and the adaptive weight strategy and random difference strategy are used to prevent the algorithm from falling into local optimum.Experiments show that the improved whale optimization algorithm works better,the convergence accuracy becomes higher,and the optimization effect is obvious in terms of task completion time.(3)The slime mold algorithm has the problem of slow convergence speed,and often falls into local minimum.An improved cross factor slime mold algorithm(ISMA)is proposed to optimize the task completion time of cloud task scheduling and reduce the task completion cost.In order to ensure the global search ability of the algorithm,an adaptive and adjustable feedback factor is added;in order to help the algorithm to increase the convergence speed,an algorithm cross factor is added;to improve the artificial bee colony search strategy,a greedy strategy is selected to retain better individuals.The experimental results show that the improved slime mold algorithm has been improved in terms of convergence speed and convergence results,the task completion time has been shortened,and the task completion cost has also been optimized.