Research Of Cloud Computing Scheduling Algorithm Based On Bayesian Model

Cloud computing is a revolution that computing science is undergoing.Cloud computing provides users with the required resources as a service.Users connect through the network,use on-demand access,pay-as-you-go,and use resources provided on the cloud platform.Among the various technologies supporting cloud computing services,task scheduling is a key technology to control resources and improve system stability,and plays a vital role in service quality.Existing workflow task scheduling algorithms in the cloud environment usually focus on satisfying the user's service foundation while improving system load capacity,reducing task completion time and computing costs.At present,many scholars and experts have conducted many researches on this issue.From the perspective of algorithm simulation,this thesis uses the Bayesian model to learn the scheduling results of existing algorithms,and then obtains new scheduling strategies.The advantage of the Bayesian model is that it is different from general statistical methods.It not only uses model information and data information,but also makes full use of prior information,and has a good prediction effect.This thesis analyzes cloud computing task scheduling algorithms and scheduling models.Based on the Bayesian model,the following researches are done:1.This thesis proposes a cloud computing task scheduling algorithm based on Naive Bayes.In the cloud computing task scheduling scenario,the task to device mapping process is abstracted into a classification process.The characteristic values are selected from the existing scheduling results,the data set is constructed,the scheduling results of the traditional algorithm are learned by using the naive bayes classifier in machine learning,the simulation of the traditional algorithm is realized,and a new scheduling strategy is formed to solve the task scheduling problem in the cloud computing environment.2.A cloud computing task scheduling algorithm based on Bayesian network isproposed.This method improves the learning ability of traditional algorithm scheduling results by enhancing the dependency relationship between data set attributes,considering the influencing factors of the task scheduling results before and after,and obtaining a new scheduling strategy.Compared with naive Bayesian cloud computing task scheduling algorithm,this scheduling strategy can better solve the task scheduling problem in cloud computing environment.3.A Bayesian task scheduling algorithm based on equipment state feedback was designed.The algorithm reduces equipment conflicts through feedback of device load status and dynamic priority.A multi-task collaborative scheduling algorithm based on device feedback is designed,and a Bayesian model is used to simulate the Bayesian task scheduling algorithm.