Issue Dependency Graphs for Automated Root Cause Detection
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Solution Overview
Problem
Troubleshooting highly distributed and heterogeneous environments is challenging due to complex dependencies between system components, leading to inefficiencies, increased downtime, and errors in manual root cause analysis.
Innovation Solution
A mechanism that automates the generation of an issue dependency graph using natural language descriptions and language models to identify the root cause of problems in computing systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual root cause analysis is performed in distributed systems, then human judgment and flexibility are applied, but the process becomes time-consuming and error-prone as system size grows
Solution Approach 1:
The patent replaces manual mechanical analysis processes with an automated AI-based system. The machine learning model automatically processes issue reports, resolves dependencies, and identifies root causes, substituting human manual analysis with automated intelligent processing to reduce time while maintaining or improving accuracy.
Solution Approach 2:
The system performs self-service by automatically analyzing its own outputs. The machine learning model continuously refines its root cause identification based on feedback loops, automatically resolving dependencies and updating its analysis without requiring continuous human intervention, thereby reducing time loss while maintaining high reliability.
2Ease of operation
If manual dependency resolution is performed, then human expertise is applied, but complexity increases as system size and number of components grow
Solution Approach 1:
The patent replaces complex manual dependency resolution with automated machine learning processing. The system automatically parses issue reports, resolves dependencies across distributed components, and constructs dependency graphs without human intervention, making the process easier while handling increasing complexity through intelligent algorithms.
Solution Approach 2:
The system segments the complex dependency analysis into manageable components by processing issue reports individually, resolving dependencies in discrete steps, and constructing the overall dependency graph incrementally. This segmentation makes the complex analysis process more manageable and scalable as system complexity increases.
3Productivity
If automated AI-based analysis is implemented, then speed and consistency of root cause identification improve, but system complexity and computational resources increase
Solution Approach 1:
The patent applies a universal machine learning model that handles multiple functions: processing issue reports, resolving dependencies, identifying root causes, and providing explanations. This multi-functional approach increases productivity across all analysis tasks while managing system complexity through a single unified model rather than multiple specialized systems.
Solution Approach 2:
The system incorporates feedback mechanisms where the machine learning model continuously refines its analysis based on the structure and content of issue reports. This feedback loop improves accuracy and speed of root cause detection while managing complexity through adaptive learning rather than requiring overly complex predetermined rules.
Data Source
AI summary
In one implementation, a device may obtain natural language descriptions of issues detected in a computing system. The device may prompt one or more language models to generate sets of possible causal dependencies between the issues based on their natural language descriptions. The device may form, using the one or more language models, an issue dependency graph that reaches consensus among the sets of possible causal dependencies between the issues. The device may use the issue dependency graph to determine a particular one of the issues as a root cause of an indicated problem in the computing system.