Method and system for monitoring software service quality based on Bayesian inference

Abstract

The invention discloses a method and a system for dynamically monitoring the software service quality based on Bayesian inference. The method comprises the following steps of: setting null hypothesis and alternative hypothesis, selecting a prior distribution function, reading a training sample, pre-processing the training sample, counting the quantity of samples meeting attributes, and updating a sample set; reshaping the total quantity of samples, the quantity of successful samples and a standard value; and calculating a Bayesian factor, and analyzing, storing and returning a monitoring result. The system comprises a controller, an observer and an analyzer, wherein the controller is used for acquiring the service statement of software, generating an analyzer of different task objectives, transmitting a service standard required to be matched to the analyzer, issuing a command to a data acquisition end, and controlling periodic acquisition; the observer is used for periodically acquiring needed service data from a data service end, and screening, classifying and rearranging information; and the analyzer is used for matching data to form a sample which can be pre-processed, transmitting into a history database for storing, analyzing data by adopting a Bayes statistical module, and storing a result into a monitoring result database.

Description

technical field [0001] The invention relates to a method and system for monitoring QoS demand indicators of running software, in particular to a method and system for monitoring software service quality based on Bayesian inference, belonging to the technical field of software service quality monitoring. Background technique [0002] In recent years, software systems are increasingly required to have high dynamics and flexibility, to be able to dynamically combine distributed and autonomous third-party components in an open environment, and to support the continuous evolution of software runtime. However, in the complex and changeable Internet environment, this dependence on third-party services will bring uncertain problems, such as changes in service component interfaces, changes in dynamic selection, changes in components themselves, or if the functions provided by each component or Different from pre-declaration, non-functional characteristics will have a serious impact a...

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Problems solved by technology

[0003] Key indicators of QoS such as availability, throughput, delay, delay variation (including jitter and drift) and loss are usually expressed as uncertainties by probability, but in many cases it is difficult to give an exact value when analyzing and evaluating QoS indicators. However, fuzzy language is used, such as "the probability of the program responding within 0.2s is 95%", "the data packet loss rate is less than 3 / 10,000", etc. To ensure the reliable operation of the system, it is necessary to deal with these uncertain fuzzies. probabilistic monitoring and diagnosis

However, the existing probabilistic monitoring methods use traditional hypothesis testing for statistics, only use a single probability value evaluation, do not use compound assumptions, the actual implementability is poor, or use the SPRT (Sequential Probability Ratio Test) method for statistics, because the sample of this method A large number fall into the neutral zone, and monitoring often fails

Therefore, the existing probabilistic monitoring technology cannot meet the needs of probabilistic monitoring

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