Research On Cloud Computing Resource Scheduling Optimization Based On Particle Swarm Optimization

Cloud computing is the most popular commercial computing service model in recent years,providing users with computing services as convenient as using hydropower.The cloud computing data center is composed of a large and expensive server,which realizes resource sharing through virtualization technology,and virtualizes one physical machine into multiple virtual machines,thereby improving the utilization efficiency of the server.Faced with massive user growth and data growth,the scale of cloud computing data centers is also growing.How to manage cloud computing resources more efficiently while meeting user QoS requirements is particularly important.Based on the particle swarm optimization algorithm,this paper introduces the tabu search idea and applies the improved particle swarm optimization algorithm to the resource scheduling problem of cloud computing.As a biomimetic algorithm,the particle swarm optimization algorithm is derived from the imitation of the predation behavior of birds in nature.In the process of optimization,the information of the population can be well shared.Therefore,the search efficiency is very high,and the parameters of the particle swarm algorithm are few.It is easier to implement than other heuristic algorithms.The particle swarm algorithm was originally designed to solve continuous problems.Later,due to its excellent performance,it was extended by many scientists to the field of discrete problem solving,especially in the field of solving NP problems.In the resource scheduling problem of large-scale clusters in the cloud computing environment,the particle swarm algorithm has good performance.However,particle swarm optimization is not a panacea.Particle swarm optimization may have a "premature" phenomenon when searching for optimal solutions.The phenomenon of "premature maturity" means that the algorithm falls into a local extremum solution prematurely and cannot jump out,so that the final result is not the global optimal solution.The tabu search algorithm simulates the process of human intelligence development,blocks the nearest search area,and directs the search away from the local extremum solution.This paper improves on the basis of the standard particle swarm optimization algorithm,and introduces the judging link in the initialization,so that the initial population is evenly distributed in the solution space,and the judgment and treatment scheme of the"premature maturity" problem is introduced.After the judgment is searched for "premature" The Repulsion operation of ARPSO algorithm is used to make the population jump out of the local extremum solution,introduce the idea of taboo search,block the optimal solution of the last few searches,and gradually guide the population to jump out of the local extremum solution to solve the"premature" problem.In this paper,the improved particle swarn optimization algorithm is applied to the resource scheduling of cloud computing.Through the Cloudsim simulation platform,its performance is tested and analyzed,and compared with the standard particle swarm optimization algorithm.The experimental results show that the scheduling efficiency is better than the standard particle swarm optimization algorithm,improve.