Network Management System Root Cause Detection via Hierarchical Graph
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Solution Overview
Problem
Existing network management systems struggle to accurately identify the root cause of network failures across different network scopes without relying on extensive data collection and computational intensive methods.
Innovation Solution
The implementation of a network management system that utilizes machine learning techniques applied to a hierarchical attribution graph, allowing for the detection of network scope failures by attributing client device failures to specific network scopes such as servers, sites, wireless networks, access points, and client devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional network management systems collect extensive data and use computational intensive methods to identify root cause of network failures, then measurement precision is improved, but use of energy and device complexity increase
Solution Approach 1:
The network scope is segmented into hierarchical levels (organization, server, site, wireless network, access point, client device). The machine learning model evaluates failures at each hierarchical level independently, processing only relevant data for each scope rather than analyzing all network data comprehensively. This segmentation reduces computational resource consumption while maintaining root cause identification accuracy.
2Measurement precision
If traditional network management systems collect extensive data to detect network scope failures, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system pre-establishes the hierarchical attribution graph structure and pre-configures the machine learning model with evaluation criteria for each network scope level. When a failure occurs, the model immediately evaluates the failure against the pre-established hierarchy without requiring extensive data collection, enabling rapid and accurate failure detection.
3Productivity
If machine learning techniques are applied to hierarchical attribution graph to detect network scope failures, then productivity is improved, but device complexity increases
Solution Approach 1:
The hierarchical attribution graph serves as an intermediary structure that organizes network elements into predefined hierarchical levels. The machine learning model uses this structured graph as a mediator to evaluate failures systematically, translating complex network relationships into a manageable hierarchical evaluation process that improves detection efficiency without excessive complexity.
Data Source
AI summary
Techniques are described by which a network management system (NMS) is configured to provide identification of root cause failure through the detection of network scope failures. For example, the NMS comprises one or more processors; and a memory comprising instructions that when executed by the one or more processors cause the one or more processors to: generate a hierarchical attribution graph comprising attributes representing different network scopes at different hierarchical levels; receive network event data, wherein the network event data is indicative of operational behavior of the network, including one or more of successful events or one or more failure events associated with one or more client devices; and apply a machine learning model to the network event data and to a particular network scope in the hierarchical attribution graph to detect whether the particular network scope has failure.