Cloud computing platform system fault prediction method based on hidden semi-Markov models

A cloud computing platform, system failure technology, applied in computing, error detection/correction, instrumentation, etc., to achieve the effect of improving feasibility and efficiency

Inactive Publication Date: 2016-08-24
CITY CLOUD TECH HANGZHOU CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, there is still a lack of a relatively mature and reliable solution in the field of fault prediction under large-scale distributed systems.

Method used

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  • Cloud computing platform system fault prediction method based on hidden semi-Markov models
  • Cloud computing platform system fault prediction method based on hidden semi-Markov models
  • Cloud computing platform system fault prediction method based on hidden semi-Markov models

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Embodiment Construction

[0041] The technical solutions of the present invention will be clearly and completely described below through specific embodiments in conjunction with the accompanying drawings.

[0042] Such as figure 1 As shown, the present invention is based on the Hidden Semi-Markov Model (HSMM) cloud computing platform real-time system failure prediction method by carefully processing and analyzing the log data in the cloud computing platform, the purpose is to be able to hide the massive log data The rich information in the system can be mined, so that accurate predictions can be made for possible future system failures, and the stability and continuity of system services can be guaranteed. Specific steps are as follows:

[0043] S100, process the log data generated by the cloud computing platform in real time, extract the error events contained therein from the unstructured log files, and then use the edit distance algorithm to merge similar error event records, and obtain The error ...

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Abstract

The invention discloses a cloud computing platform system fault prediction method based on hidden semi-Markov models. The cloud computing platform system fault prediction method includes the following steps that error events are extracted from mass log files, and endowed types and time information of the error events are obtained; repeated reports of the same error events are combined into one event; multiple continuous events are combined into event sequences, and the event sequences are divided into fault related event sequences and fault unrelated event sequences; according to a sequence likelihood value, all the event sequences are clustered; noise elimination is carried out in each cluster; the sequence likelihood value between the event sequences extracted in real time and the fault related hidden semi-Markov model (HSMM) and the sequence likelihood value between the event sequences extracted in real time and the fault unrelated HSMM are each calculated, and a Bayes classifier is used for calculation to judge whether a system fails or not. Based on the machine learning theory, the function of predicting system faults in real time by a cloud computing platform is achieved, accurate prediction results can be provided, and overall performance is higher.

Description

technical field [0001] The invention relates to cloud computing platform system fault prediction technology, in particular to a cloud computing platform real-time system fault prediction method based on hidden semi-Markov model. Background technique [0002] In the traditional cloud computing platform operation and maintenance work, when the system fails, the operation and maintenance personnel often need to spend a certain amount of time to troubleshoot and solve the problem, which also leads to system service instability or even service suspension. Therefore, making accurate predictions of possible faults in the system in advance can reserve enough fault response time for system operation and maintenance personnel to deal with problems in advance, thereby avoiding the occurrence of system faults, which is important for improving the stability of system services and The efficiency of operation and maintenance work has great significance. [0003] A cloud computing system i...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F11/30
CPCG06F11/3079G06F11/3006
Inventor 曹晖寿黎但张之宣
Owner CITY CLOUD TECH HANGZHOU CO LTD
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