Software service quality monitoring method and system based on weighted naive Bayes classifier

Abstract

The invention discloses a software service quality monitoring method and system based on a weighted naive Bayes classifier. By the constructed weighted naive Bayes classifier, the QoS (quality of service) of software is judged to determine that the QoS belongs to a standard satisfying category, a standard unsatisfying category or an incapable of judging category. During training, an influence factor combination is set, influence factors refer to software's influence on QoS, and the weight and the priori knowledge of the influence factor combination are calculated; during monitoring, the classifier acquires monitoring results, and analyzes, stores and returns evaluation to a data server. The system comprises a controller, an observer, a trainer, an optimization sample set and an analyzer, wherein the controller collects different QoS statements, issues data standard instructions required by different QoS, transmits QoS standards which need to be matched with data sets to the trainer, and controls collecting cycle and frequency; a database collects the monitoring results of the analyzer; a service capability evaluating module returns software monitoring results and evaluation results to data service equipment.

Description

technical field [0001] The invention relates to a software service quality monitoring method and system based on a weighted naive Bayesian classifier, in particular to the dynamic weighted monitoring of QoS demand indicators during operation, and belongs to the field of information technology. Background technique [0002] Web service technology adapts to any type of Web environment, including Internet, Intranet and Extranet, and realizes the communication between enterprises and enterprises, enterprises and consumers. From the perspective of the key technologies of Web services, Web services still have a lot of research space and challenges. One of the most important issues in user needs is the quality of service (Quality of Service). The key to the success of the application. Today, when Service-Oriented Architecture is widely used, software systems can dynamically combine some loosely coupled components (that is, Services) with a unified interface definition. However, in...

AI Technical Summary

Problems solved by technology

The current probabilistic monitoring methods use hypothesis testing for statistics, and there are also monitoring methods based on Bayesian factors. The former only uses a single probability value for evaluation, and the actual implementability is poor. The error rate of the monitoring results of the latter needs to be reduced. More importantly, Existing methods ignore the influence of the environment on the monitoring results. Different environments will affect our probability monitoring results. These environments include the user's location, network, server CPU, RAM, I / O, etc. For example, users are in different regions. May experience different QoS when using the same service

Therefore, the existing probabilistic monitoring technology has been unable to meet the needs of probabilistic monitoring

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