Root Cause Discovery Engine for Predicted-Outcome Troubleshooting
Find Innovative SolutionsGenerate Solutions
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 provide actionable causes or factors for observed outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual human operators are used to analyze large datasets and identify root causes, then comprehensive understanding and troubleshooting capability are achieved, but time consumption and operational complexity increase significantly
Solution Approach 1:
The patent introduces an intermediary system consisting of a dependency graph and machine learning model that mediates between the complex IT infrastructure data and the human operator. The system automatically analyzes operations data, identifies correlations between metrics and events, and presents potential root causes to operators, thereby reducing their analytical burden while maintaining accuracy.
Solution Approach 2:
The patent replaces the mechanical manual analysis process with an automated computational system. Instead of human operators manually examining datasets and reasoning through dependencies, a machine learning model processes the data automatically, identifying patterns and correlations that would be difficult for humans to detect manually.
2Measurement precision
If comprehensive manual analysis of IT infrastructure data is performed, then accurate root cause identification is achieved, but device complexity and operational difficulty increase
Solution Approach 1:
The system performs self-service by automatically analyzing operations data and identifying root causes without requiring human operators to manually examine complex datasets. The machine learning model independently processes the data, identifies correlations, and generates diagnostic results, freeing operators from complex analytical tasks.
Solution Approach 2:
The dependency graph and machine learning model serve as intermediaries that handle the complexity of data analysis. They translate complex infrastructure data into actionable insights, presenting simplified diagnostic information to operators while maintaining comprehensive analysis capabilities.
3Productivity
If automated machine learning analysis is implemented, then troubleshooting speed and automation extent are improved, but system complexity and measurement precision requirements increase
Solution Approach 1:
The machine learning model serves multiple functions: it analyzes operations data, identifies correlations between metrics and events, ranks potential root causes, and presents diagnostic results. This multi-functionality consolidates what would otherwise require multiple separate systems into a single automated platform.
Solution Approach 2:
The system changes the parameters of analysis by using machine learning algorithms that can process large volumes of data and identify non-obvious correlations. The model transforms raw operations data into structured diagnostic information, changing the state from unprocessed data to actionable insights.
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.