Abstract
With the continuous development of Internet, cloud computing, and other technologies, build a cloud platform based on Cloud Computing Center, but how to effectively carry out operation and maintenance and face users to ensure the continuity and effectiveness of the platform is extremely important. In view of these needs and limitations, this paper introduces the multipoint mapping algorithm, combs the statistical methods of platform cloud traffic, carries out platform data traffic by classification, constructs the data traffic optimization management model, analyzes the relevant data samples, carries out statistical calculation for data diversion tasks, analyzes and processes the priority indicators, and forms the final results through continuous iteration, realizing the management of data flow optimization virtual simulation of big data cloud platform. Simulation results show that the multipoint mapping algorithm is effective and can effectively support the data flow of big data cloud platform and optimize virtual simulation management.
Highlights
With the continuous development of cloud computing, Internet of things, and other technologies, cloud computing centers are gradually rising everywhere [1]
According to the data flow corresponding to the big data cloud platform, the traffic data of the subunit are counted, as shown in the following formula: c [c(0), c(1), . . . , c(e − 1)]k
Through the statistical analysis of the task volume of the subunits of the big data cloud platform, the corresponding dynamic priority tasks are obtained, which provides a basis for data traffic optimization and virtual simulation management
Summary
With the continuous development of cloud computing, Internet of things, and other technologies, cloud computing centers are gradually rising everywhere [1]. E objective function and constraint conditions of data traffic diversion task of big data cloud platform are used for calculation, as shown in the following formula:. For the data diversion of big data cloud platform, firstly, all sample data need to be statistically analyzed to classify the current data flow, so as to realize virtual simulation management and calculate the tasks of current subunits, so as to obtain the corresponding dynamic priority tasks. According to the data flow corresponding to the big data cloud platform, the traffic data of the subunit are counted, as shown in the following formula:. Through the statistical analysis of the task volume of the subunits of the big data cloud platform, the corresponding dynamic priority tasks are obtained, which provides a basis for data traffic optimization and virtual simulation management.
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