Cloud Network Failure Auto-Correlation for Misconfiguration Root Cause
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Solution Overview
Problem
Troubleshooting network misconfigurations in complex cloud environments is manually intensive and computationally infeasible, making it difficult to determine the root cause of network failures or errors, especially with multiple changes occurring.
Innovation Solution
A method and system using a machine learning model trained with expert rules to auto-correlate cloud network failures by receiving triggers, determining the scope of analysis, selecting appropriate analyzers, and performing an analysis to identify the root cause, utilizing knowledge-based dependency graphs and machine learning techniques to limit the analysis to a relevant subset of the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual troubleshooting of network misconfigurations is performed, then accurate root cause identification can be achieved, but the process becomes manually intensive and time-consuming
Solution Approach 1:
The system performs self-diagnosis by automatically analyzing network events, configuration changes, and dependencies to identify root causes without human intervention. The auto-correlator autonomously correlates triggers with configuration changes and determines scope of analysis, eliminating the need for manual troubleshooting while maintaining accurate root cause identification.
Solution Approach 2:
The patent replaces manual mechanical troubleshooting processes with an automated software-based analysis system. The machine learning model and analyzer module substitute human analysts, automatically processing network data, evaluating configuration changes, and determining root causes through computational algorithms rather than human inspection.
2Reliability
If brute force analysis of every network change and configuration is performed, then comprehensive coverage is achieved, but the computational complexity becomes infeasible
Solution Approach 1:
The system segments the network analysis into distinct hierarchical scopes: account scope, organization scope, and network scope. This segmentation allows the analyzer to focus computation on relevant portions of the network rather than performing exhaustive brute-force analysis of every component, reducing computational complexity while maintaining reliability through targeted analysis.
Solution Approach 2:
The patent applies local quality by determining the scope of analysis based on the specific trigger and configuration changes identified. Rather than uniformly analyzing the entire network, the system adapts the analysis scope to the local context of the failure, concentrating computational resources on the specific area affected by the misconfiguration while ignoring unrelated network segments.
3Measurement precision
If analysis scope is expanded to cover the entire cloud network, then complete failure detection is achieved, but the analysis time and computational resources increase significantly
Solution Approach 1:
The system dynamically adjusts the scope of analysis based on the specific trigger received and the configuration changes identified. The analyzer module modifies the analysis boundaries in real-time, expanding or contracting the scope according to the nature of the failure and its potential impact, rather than using a fixed comprehensive scope for all analyses.
Solution Approach 2:
The patent performs preliminary actions by first receiving and analyzing triggers, then comparing them against configuration changes to generate a subset before determining the final scope of analysis. This preliminary filtering step identifies the relevant configuration changes early in the process, allowing the system to limit subsequent analysis to only those areas likely to contain the root cause, reducing overall analysis time.
Data Source
AI summary
Analysis of a root cause of errors within a cloud network is manually complex and computationally intensive. Methods and systems are provided to determine a subset of elements of the cloud network to analyze, and to identify a subset of analyzers for analyzing the subset of elements to determine the root cause for the error. Thus, when configuring a network, a user may be provided with an identification of the root cause of error, enabling the user to quickly identify and correct the error.