Micro-service fault diagnosis method based on neural network and storage medium

A technology of fault diagnosis and neural network, applied in non-redundancy-based fault handling, neural learning methods, biological neural network models, etc., can solve problems such as topological indirect fault propagation, inability to locate directly, failure to identify faults, etc., to achieve Avoid infinite recursion problems, improve user experience, and perform real-time diagnosis

Pending Publication Date: 2022-01-07
大唐互联科技(武汉)有限公司 +1
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AI Technical Summary

Problems solved by technology

It can be seen that there is indirect fault propagation in the topology called between services, and the problem cannot be directly located
Therefore, the high complexity and dynamic nature of microservices architecture makes troubleshooting difficult
First of all, it is difficult to obtain a fixed service topology relationship, and this static troubleshooting method cannot be applied to the situation where service calls change frequently; even if we know the topology relationship between services, due to the indirect propagation of faults, we still lack Effective dynamic diagnosis mechanism to determine the root cause
Furthermore, time-series anomaly detection algorithms based on a single metric often fail to identify the root cause of failures because a single metric is not sufficient to characterize the anomalies occurring in various services

Method used

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  • Micro-service fault diagnosis method based on neural network and storage medium
  • Micro-service fault diagnosis method based on neural network and storage medium

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Embodiment Construction

[0035] Various exemplary embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be noted that the relative arrangements of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

[0036] At the same time, it should be understood that, for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0037] The following description of at least one exemplary embodiment is merely illustrative in nature and in no way taken as limiting the invention, its application or uses.

[0038] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in further detail below in conjunction with specific embodiments and with refer...

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Abstract

The invention provides a micro-service fault diagnosis method based on a neural network. The method comprises the following steps: acquiring a service link diagram; based on the service link diagram, collecting and storing service operation data; analyzing the operation data to obtain indexes influencing service operation; analyzing abnormal fluctuation information in the indexes, and constructing an abnormal index graph; outputting candidate root causes according to the abnormal index graph based on a random walk algorithm; and inputting the candidate root causes into a pre-trained fault diagnosis network model to verify the accuracy, thereby obtaining a service fault diagnosis result. According to the method, the service fault of the micro-service architecture can be diagnosed in real time, and the accuracy of the diagnosis result is relatively high.

Description

technical field [0001] The present invention relates to the technical field of microservice architecture service fault diagnosis, in particular to a neural network-based microservice fault diagnosis method and a storage medium. Background technique [0002] As microservice architectures become more popular, the performance of microservices is critical, as microservice failures can degrade user experience and result in financial losses. Effectively locating the root cause of a failure can help restore service and mitigate losses. In the microservice architecture, an application is often decomposed into multiple microservices, and the web application calls services running on different hosts and containers through the gateway (see Figure Service Call). It can be seen that there is indirect fault propagation in the topology called between services, and the problem cannot be directly located. Consequently, the high complexity and dynamics of microservice architectures make tro...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F11/07G06K9/62G06N3/04G06N3/08
CPCG06F11/0709G06F11/079G06F11/0766G06N3/04G06N3/08G06F18/241
Inventor 李学徐军李军章书乐詹开洪何宁波
Owner 大唐互联科技(武汉)有限公司
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