Particle Swarm Optimization Algorithm In Task Scheduling Of Cloud Computing

Cloud computing technology has become one of today's most popular network technology.TThe rise of cloud computing technology is not only the product of the rapid development of information technology,but also the higher requirement of human society for life and work.Cloud computing technology virtualized the concept of personal computers,but through a third party to achieve the computer's storage and computing tasks,and then through the on-demand way to provide to the public use.So how to quickly and efficiently schedule and use huge resources in the third-party data center has become the key to the development of cloud computing technology.Firstly,the successful application of particle swarm algorithm in cloud computing task scheduling,in order to avoid the defects of the standard particle swarm algorithm is easy to fall into local optimum,thus introduced the Chebyshev chaotic disturbance strategy through the perturbation strategy makes the particle swarm algorithm in the late operation has the ability to jump out of local optimum,the particle swarm algorithm can get better global optimization excellent results.Cloudsim simulation platform is verified through the use of cloud computing,the experimental results show that the improved particle swarm algorithm compared with other traditional algorithms in cloud computing to obtain better scheduling results of task scheduling in a shorter period of time.Secondly,based on the introduction of Chebyshev chaotic disturbance strategy at the same time,also joined the dynamic inertia weight strategy,the improved particle swarm algorithm has the ability to jump out of the local optimum,can also according to the actual problems of dynamic regulation of its global search and local search ability.And the improved algorithm is applied to the task scheduling in cloud computing,Cloudsim simulation platform is verified through the use of cloud computing,the experimental results show that the improved algorithm has better scheduling results than the improved algorithm,and the shorter the time.Finally,the multi-objective particle swarm algorithm is studied and studied,and it is applied to cloud computing task scheduling.The multi-objective particle swarm optimization is improved by introducing the dynamic inertia weightingstrategy and the adaptive evolutionary learning strategy.The experimental results show that the improved multi-objective particle swarm optimization algorithm can obtain better scheduling results in the multi-objective cloud computing task scheduling in a short period of time by using the cloud computing simulation platform Cloudsim.