Research On Cloud Computing Task Scheduling Strategy Based On Particle Swarm Optimization And Imperial Competition Hybrid Algorithm

Cloud computing uses virtualization technology to integrate all kinds of computing,storage,network broadband and other physical resources into a shared cloud service resource pool,then the task scheduling algorithm allocates resources for the tasks submitted by the user.Task scheduling algorithm is one of the most important technologies in cloud computing.Ensuring the stable and efficient operation of cloud computing platform,efficient task scheduling algorithm can shorten the processing time of the task,reduce the cost of cloud services,and ensure the economic benefits of cloud computing service providers.The cloud computing environment is very complex,and the traditional scheduling algorithms have been unable to meet the needs of cloud computing task scheduling.The new intelligent scheduling algorithms improve the performance of task scheduling to a certain extent,but they are not mature and stable enough,and have some defects in convergence accuracy and stability.Mastering the existing task scheduling model and algorithm,the motivation of this paper is to work out a scheduling algorithm to solve the problem of cloud computing by analyzing the key technologies and characteristics of cloud computing and task scheduling algorithm.The main work is as follows:(1)The basic theory of cloud computing,cloud computing task scheduling,and cloud computing task scheduling algorithm is introduced,then the basic principle and mathematical model of particle swarm optimization algorithm and imperial competition algorithm are analyzed in detail,finally,the development of these two algorithms and the improvement are summarized.(2)Comparing and analyzing the characteristics of particle swarm optimization and imperial competition algorithm,a hybrid algorithm of particle swarm optimization and imperial competition is proposed in view of the disadvantages of the colony competition algorithm,which has no autonomous learning ability,can not record the best historical information,and the convergence speed of particle swarm optimization algorithm is too fast.The biologically inspired particle swarm optimization algorithm and the social inspired imperial competition algorithm are combined to achieve the complementary effect.Incorporating the idea of particle swarm algorithm to make the colony have the characteristics of the particle,the inertia weight is adjusted adaptively to adjust the pace of the colony in view of the lack of an effective control mechanism in the colony algorithm to adjust the distance and angle.(3)The hybrid algorithm of particle swarm and imperial competition is applied to cloud computing task scheduling.This paper designs the coding form and fitness function,then the experiment is carried out on the cloud computing simulation platform Cloudsim,The experimental results are analyzed and compared with the algorithm before improvement.The experimental results show that the proposed algorithm has better performance.