Root Cause Discovery Engine Using Dependency Graph Analysis
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Solution Overview
Problem
Identifying root causes in complex IT infrastructures is challenging due to their complexity and the difficulty in managing large datasets, especially with technologies like micro-services and distributed or cloud environments, requiring manual human intervention and extensive data analysis.
Innovation Solution
A root cause discovery engine that utilizes a machine learning model and dependency graph to analyze operations data, identifying correlations between metrics, events, and conditions to automatically detect and resolve issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual troubleshooting by specialized human operators is used, then root cause analysis can be performed with comprehensive knowledge, but the process is time-consuming and requires extensive manual intervention
Solution Approach 1:
The system pre-generates a dependency graph mapping relationships between IT infrastructure entities before problems occur. When an issue arises, this pre-established graph enables immediate analysis without requiring operators to manually build relationship maps during troubleshooting, thus reducing time loss while maintaining analysis accuracy.
Solution Approach 2:
An automated analysis system acts as an intermediary between IT infrastructure data and human operators. This intermediary automatically processes operations data, correlates events using the dependency graph, and presents root cause findings to operators, eliminating the need for manual data sifting while preserving comprehensive analytical capabilities.
2Reliability
If comprehensive manual analysis of large datasets is performed, then possible causes can be identified, but the complexity of managing and analyzing the data increases
Solution Approach 1:
The system segments the complex IT infrastructure into discrete entities (servers, applications, databases, network components) and represents their relationships as a structured dependency graph. This segmentation transforms unmanageable raw data into organized, queryable units that can be systematically analyzed to identify root causes while reducing overall data management complexity.
Solution Approach 2:
Manual mechanical analysis of large datasets is replaced with automated computational processing. The system uses algorithms to traverse the dependency graph, correlate events across segmented entities, and identify root causes automatically, maintaining high detection accuracy while eliminating the complexity burden of manual data management.
3Ease of operation
If traditional manual troubleshooting approaches are used, then operators can eliminate possible causes systematically, but the process requires extensive specialized knowledge and manual effort
Solution Approach 1:
The system enables self-service root cause analysis by automatically processing operations data and generating diagnostic findings without requiring operator intervention in the analysis process. The dependency graph and event correlation algorithms autonomously identify root causes, presenting results to operators who simply need to review and act on the findings, thus improving ease of operation while maximizing automation.
Data Source
AI summary
The disclosed technology relates identifying causes of an observed outcome. A system is configured to receive an indication of a user experience problem, wherein the user experience problem is associated with observed operations data including an observed outcome. The system generates, based on the observed operations data, a predicted outcome according to a model, determines that the observed outcome is within range of the predicted outcome, and identifies a set of candidate causes of the user experience problem when the observed outcome is within range of the predicted outcome.