A method and system for determining fault of heterogeneous system based on machine learning

Abstract

The invention provides a method and system for determining faults of a heterogeneous system based on machine learning, and the method comprises the steps: carrying out the analysis of the safety of historical system faults and major events, preliminarily building case library data and a fault tree model, arranging and analyzing index data and labeling data, and respectively training data models ofdifferent application scenes; according to the collected current index data and the data model, calculating and analyzing the operation health condition of the system, and triggering fault diagnosisand alarm for the grabbed abnormal index data; and automatically diagnosing a fault reason according to a relation graph established by machine learning and the collected abnormal stack annotation data, determining a fault repair scheme according to the fault reason, and triggering fault repair. According to the method and the system, the dependence of operation and maintenance personnel on professional business knowledge is reduced, intelligent and rapid fault discovery and fault generation reason diagnosis are realized through machine learning, self-repair is automatically completed, and theoperation safety and stability of the distributed heterogeneous system are greatly improved.

Description

technical field [0001] The present invention relates to the field of machine learning, and more specifically, to a method and system for determining faults of heterogeneous systems based on machine learning. Background technique [0002] With the increasing scope of computer applications and the advancement of technology, large-scale distributed computing has become a reality, and the distributed heterogeneous system (DHS Distributed Heterogeneous System) has gradually become an effective tool to solve complex application problems. [0003] Due to the large number of nodes in the large-scale distributed heterogeneous system, its own complex structure and complex business logic, a system failure may cause abnormalities in multiple monitoring indicators and a large number of test failures. It is difficult for system administrators to quickly and accurately diagnose the cause of the failure. At the same time, there are often blind spots in the monitoring of equipment and softwa...

AI Technical Summary

Problems solved by technology

[0010] In order to solve the technical problem of difficulty in determining the cause of a fault in a distributed heterogeneous system and low efficiency, the present invention provides a method for determining a fault in a heterogeneous system based on machine learning, the method comprising:

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