Multivariate QOS monitoring method for web services in mobile edge environment

Abstract

The invention discloses a web service multiple QoS monitoring method in a mobile edge environment. This method collects the location information of the edge server and the multivariate QoS attribute data of Web services stored in the edge server, performs data preprocessing on it, and then realizes multivariate QoS monitoring in the edge environment based on the Bayesian classifier. During data preprocessing, after removing the samples with missing data, the weights of each attribute are quantified based on the rough set theory, and the comprehensive attribute values ​​of the samples are calculated to obtain the sample data stream corresponding to the edge server. In the training phase, a Bayesian classifier is constructed by learning some samples and calculating the prior probability for each edge server. In the monitoring process, consider the user's mobility and call the corresponding classifier, so as to update the attribute weight, and finally realize the comprehensive and effective monitoring of the QoS of the service.

Description

technical field [0001] The invention relates to a method for monitoring multiple QoS of Web services, in particular to a method for monitoring multiple QoS of Web services in a mobile edge environment, and belongs to the technical field of the Internet. Background technique [0002] The explosive growth of data and the increasing demand of people make cloud computing gradually expose its shortcomings, such as high energy consumption of data transmission, insufficient timeliness of data processing, and lack of guarantee of data privacy, etc. Mobile edge computing came into being in this context, and its distributed deployment of multiple nodes can process and store data close to the data source, thus sharing most of the computing and transmission pressure of cloud computing, thus solving the above problems. There is no doubt that moving web services to edge servers will bring people a better experience, such as faster response time, greater throughput, and better reliability....

AI Technical Summary

Problems solved by technology

However, in the face of short-term and fast-changing data in the mobile edge environment, existing methods may cause deviations in monitoring results due to user mobility, and the impact of the importance of attributes on monitoring results is not considered in the monitoring process.

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