Web service qos monitoring method in mobile edge environment

Abstract

The invention discloses a Web service QoS monitoring method in a mobile edge environment. According to the method, user and service information and QoS attribute data of Web services are collected andsubjected to data preprocessing to obtain a data set conforming to edge environment characteristics, and QoS monitoring under the edge environment is achieved through a Gaussian hidden Bayesian probability monitoring method. When data preprocessing is carried out, after samples with missing data are removed, clustering division is carried out according to geographic positions of services, and position information and QoS data sample flow of an edge server are obtained. A part of samples are learned, the prior probability is calculated for each edge server, meanwhile, the value of a parent attribute is calculated to weaken the dependency of values between attributes, a Gaussian hidden Bayesian classifier is constructed, and therefore the accuracy of a monitoring result is improved. In themonitoring process, the mobility of the user is considered, the corresponding classifier is called, the KNN algorithm is combined, and finally the monitoring result is obtained.

Description

technical field [0001] The invention relates to a method for monitoring Web service QoS, in particular to a method for monitoring Web service QoS in a mobile edge environment, and belongs to the technical field of the Internet. Background technique [0002] With the promotion of traditional Web services by new technologies such as cloud computing and big data, the demand for services between enterprises and even individuals is gradually changing to the interaction between services. Quality of Service (QoS) is a set of non-functional attributes of Web services, which is an important indicator to measure the quality of third-party services. Each QoS attribute represents the quality information of a certain aspect of Web services, such as response time, throughput, and reliability. sex etc. In a complex and ever-changing environment, the operating environment and quality of Web services are changing all the time. In order to enable services to adapt to real-time changing envir...

AI Technical Summary

Problems solved by technology

However, in the face of short-term and fast-changing data in the mobile edge environment, the existing methods may cause deviations in monitoring results due to user mobility, and the dependency between attribute values ​​​​is ignored in the monitoring process.

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