SaaS software performance problem identification method based on hidden Markov random field

Abstract

The invention discloses an SaaS software performance problem recognition method based on a hidden Markov random field, and the method comprises the steps: researching an SaaS software performance problem recognition model based on a hidden Markov random field HMRF, and establishing a maximum posterior probability MAP estimation model of a performance problem through the HMRF; establishing a relationship between MAP estimation and HNN energy, and providing an updating rule to ensure convergence; and designing an algorithm based on the expectation maximization (EM) to obtain the optimal parameters of the estimation model, and recursively estimating the model parameters in the EM framework based on the observation data. The method has the beneficial effects that the system overhead is small,the performance problem can be accurately identified, and the operation and maintenance management personnel can be really assisted to recover the service capability of the SaaS software.

Description

technical field [0001] The invention belongs to the technical field of performance analysis, and specifically relates to an identification method based on a Hidden Markov Random Field (HMRF) for performance problems generated during service-oriented software operation. Background technique [0002] SaaS software provides software to users in the form of services, and quality of service (QoS) is undoubtedly a decisive factor in determining user satisfaction. As an important quality of service attribute of SaaS software, performance directly affects user experience. In the dynamically scalable operating environment provided by cloud computing, if the SaaS software responds to various service requests, especially the average time for responding to service requests from tenants is too long, the software service does not meet the Service Level Objective (SLO) , and loses availability, the service is said to have a performance problem. When there is a performance problem in the ...

AI Technical Summary

Problems solved by technology

At present, the information that can be provided in the real software environment is often lacking, which affects the timeliness and accuracy of performance problem identification

[0007] (2) Existing performance problem analysis methods are often difficult to meet the requirements of SaaS software in terms of timeliness and accuracy

However, since the SaaS software is deployed in the cloud platform environment, the application software or service interactions between the various layers are frequent, resulting in massive log data generated by each component in the system, and many of them are multi-dimensional data full of noise, which not only increases the It is difficult to identify SaaS software performance problems in traditional ways, and it reduces the timeliness and accuracy of identification

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