Fault prediction method and fault prediction device for distributed system

Abstract

The invention discloses a fault prediction method and a fault prediction device for a distributed system. The method comprises the following steps: acquiring original log data in various sliding time windows of a distributed system; establishing a log knowledge graph based on the original log data; extracting feature information based on the log knowledge graph; generating a fault prediction model based on the feature information; and executing a corresponding fault prediction operation based on the fault prediction model. According to the invention, original log data of the distributed system in the operation process is extracted based on multiple sliding time windows, log data in various operation states can be effectively extracted, various operation conditions of the distributed system can be comprehensively represented, and on the basis, the log knowledge graph is established and the characteristics of the log data are extracted; fault prediction is carried out through a semi-supervised learning type neural network model, so that the fault prediction accuracy of the distributed system is greatly improved, and the actual requirements of enterprises are met.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis, in particular to a fault prediction method for a distributed system and a fault prediction device for a distributed system. Background technique [0002] With the continuous development of science and technology and the continuous development of business, technicians propose distributed systems to meet actual business and functional requirements. However, errors or abnormalities may occur in the application process of distributed systems, which will cause troubles for users. [0003] In order to solve the above technical problems, technicians proposed a fault prediction method for distributed systems, by extracting log data in distributed systems, specifically extracting log data corresponding to multiple time intervals with the same step size, after preprocessing Input the pre-trained fault prediction model and generate the fault prediction result for the next time interval. [0004] Ho...

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Problems solved by technology

[0004] However, in the actual application process, although the distributed system is composed of multiple sub-modules, there is a propagation mechanism between each sub-module. Therefore, in the process of fault prediction of the distributed system in the prior art, there is a problem that the fault prediction information is not accurate. Technical problems such as accuracy and incomplete prediction considerations lead to inaccurate prediction results and low prediction efficiency when predicting faults in distributed systems, which cannot meet diverse fault requirements

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