Cloud Network Failure Correlation for Root Cause Isolation
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Solution Overview
Problem
Troubleshooting network misconfigurations in cloud environments is manually intensive and computationally infeasible, making it difficult to determine the root cause of network failures and their impact in complex, dynamic cloud systems.
Innovation Solution
A cloud network analyzer system that uses a knowledge-based dependency graph and machine learning techniques to auto-correlate configuration changes with network failures, limiting analysis to a subset of the network based on error triggers and analyzers to efficiently determine the root cause.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual troubleshooting methods are used to analyze network misconfigurations, then analysis precision can be maintained, but productivity and time consumption deteriorate significantly
Solution Approach 1:
The system segments the complex network analysis task into distinct phases: error trigger identification, dependency graph traversal, configuration change correlation, and root cause determination. This segmentation allows automated processing of each phase while maintaining analytical precision, resolving the contradiction between manual accuracy and automated speed.
Solution Approach 2:
The patent introduces an intermediary automated analysis system that acts as a bridge between manual troubleshooting expertise and computational processing. This intermediary uses knowledge graphs and machine learning to translate manual analytical methods into automated operations, achieving both speed and accuracy.
2Measurement precision
If brute force analysis of every network change and configuration is performed, then measurement precision improves, but computational complexity and resource consumption become infeasible
Solution Approach 1:
The system divides the network into relevant segments based on error triggers and dependency relationships. Instead of analyzing every configuration change across the entire network, it segments analysis to only affected portions, dramatically reducing computational complexity while maintaining precision for the specific error being investigated.
Solution Approach 2:
The patent applies local quality by concentrating analytical resources on specific network segments and configuration changes that are locally relevant to the error trigger. Rather than uniform brute force analysis everywhere, the system intensifies analysis quality only where needed based on dependency graphs and error propagation patterns.
3Reliability
If comprehensive analysis of all configuration changes is performed, then reliability of root cause determination improves, but time consumption and operational complexity worsen
Solution Approach 1:
The system performs preliminary actions by pre-building knowledge graphs of network dependencies, configuration relationships, and error propagation patterns before actual troubleshooting occurs. When an error triggers, this pre-prepared structure enables rapid reliable determination without time-consuming comprehensive analysis at the moment of failure.
Solution Approach 2:
The patent implements feedback mechanisms where the system learns from previous troubleshooting cases and error patterns. This feedback refines the dependency graphs and analysis algorithms over time, improving reliability of root cause determination while reducing the time needed for each subsequent analysis through accumulated knowledge.
Data Source
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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.