Root Cause Discovery Engine for Distributed IT Troubleshooting
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Solution Overview
Problem
Identifying root causes in complex IT infrastructures is challenging due to their complexity and the need for manual, time-consuming analysis by highly specialized human operators, especially with the advent of micro-services and distributed environments.
Innovation Solution
A root cause discovery engine utilizing machine learning models and dependency graphs to analyze operations data, identify correlations between metrics, events, and conditions, and provide automated cause analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis by highly specialized human operators is used to identify root causes, then analysis accuracy can be maintained through expert knowledge, but analysis time and operational complexity increase significantly
Solution Approach 1:
The patent introduces an automated analysis system that acts as an intermediary between the complex IT infrastructure data and the human operators. This system processes operations data, generates dependency graphs, and identifies potential root causes automatically, serving as a mediator that reduces the time burden on human experts while maintaining analysis quality through automated pattern recognition and correlation analysis.
2Reliability
If comprehensive monitoring of all IT infrastructure entities is implemented, then complete visibility into system state is achieved, but system complexity and data processing requirements increase
Solution Approach 1:
The patent extracts only the most relevant entities and operations data necessary for root cause analysis from the comprehensive IT infrastructure. Rather than monitoring and processing all possible data points, the system selectively extracts entities involved in the problematic service or application and their direct operational data, reducing monitoring complexity while maintaining sufficient visibility for effective root cause identification.
3Productivity
If automated analysis systems are deployed to reduce manual intervention, then analysis speed and productivity improve, but system complexity and initial resource requirements increase
Solution Approach 1:
The automated analysis system is segmented into distinct functional modules: data collection from IT entities, operations data processing, dependency graph generation, root cause identification algorithms, and result presentation. This segmentation allows each module to be independently developed, tested, and optimized, reducing overall system complexity while enabling automated high-speed analysis that improves troubleshooting productivity.
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.