Method for predicting state of software system based on hidden Markov model

Abstract

A method for predicting the state of a software system based on a hidden Markov model comprises the following steps: building a training sample set, and gathering samples in the training sample set into k clusters by a K mean clustering algorithm; building the hidden Markov model: lambda = [pi, A, B], using the k clusters in the training sample set as the observation states of the model, and using the system state as a hidden state; training the hidden Markov model so as to obtain a new hidden Markov model (as shown in the Specification); utilizing collected system observation values and the new hidden Markov model (as shown in the Specification) to predict the actual state of the system. Modeling is carried out on the relationship between system actual states and system observation parameters based on the hidden Markov model, the system actual state is predicted according to the system observation values, and the system software state can be accurately predicted according to the system observation values. Possible problems can be found and handled by operation and maintenance staff as soon as possible, function degrading or system breakdown is avoided, and the predicting accuracy is high.

Description

technical field [0001] The invention relates to the technical field of computer software, in particular to a software system state prediction method based on a hidden Markov model, which is mainly applied to the operation state prediction of a large-scale software management system. Background technique [0002] With the popularization and application of computers, various enterprises have higher and higher demands on large-scale software management systems. All kinds of software management systems have become an important guarantee for the safe, reliable and stable operation of various enterprises. Therefore, it is urgent to propose a system evaluation and prediction mechanism based on evaluating the operating state of the software system to improve the reliability of its operation and ensure the safe use of all functions in the software system. However, in terms of software state prediction, the development of its theory and technology is still in a preliminary stage. ...

AI Technical Summary

Problems solved by technology

[0004] The current mainstream prediction method is mainly based on the time series method, which finds a certain pattern from the previous system state sequence and then infers the future system state. The prediction accuracy is low, and this method does not explore the relationship between the actual state of the system and the observed parameters of the system.

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