Issue Signature Generation for Computing System Root Cause Analysis
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Solution Overview
Problem
Current support mechanisms in computing systems face challenges in quickly identifying the root cause of issues and determining their scope, as users often provide unstructured descriptions of problems, and existing log data is insufficient for diagnosing issues across multiple users.
Innovation Solution
A system that detects problems, generates an issue signature mapping issues to failed components and product functionality, and aggregates diagnostic data to identify root causes and potential solutions, enabling automated analysis and visualization of problem trends.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If general log information is collected from users, then diagnostic data is available for analysis, but the information is insufficient to quickly identify root causes across multiple users
Solution Approach 1:
The patent combines diagnostic data from multiple users experiencing similar issues to create aggregated problem signatures. By merging individual log information with collectively gathered data, the system transforms insufficient individual diagnostics into comprehensive multi-user diagnostic profiles that enable accurate root cause identification.
Solution Approach 2:
The system proactively collects and structures diagnostic data in advance by creating problem signatures that map issues to failed components and product functionality. This preliminary organization of diagnostic information enables rapid root cause analysis when issues occur, rather than waiting to gather data after the fact.
2Ease of operation
If support engineers manually analyze each user's problem description, then individual issues can be diagnosed, but it is difficult to determine the scope of impact across other users
Solution Approach 1:
The system implements feedback loops where problem signatures generated from individual user issues are compared against a growing database of known problems. This feedback mechanism automatically determines the scope of impact by identifying whether current issues match patterns from other users, enabling support engineers to assess widespread impact without manually analyzing each case.
Solution Approach 2:
The automated signature generation and comparison system performs self-service diagnostic analysis, automatically determining problem scope and impact without requiring manual intervention from support engineers for each individual case analysis.
3Ease of operation
If unstructured text descriptions are collected from users, then problem reporting is simple, but the descriptions are not enough to diagnose the problem
Solution Approach 1:
The patent introduces problem signatures as an intermediary layer between unstructured user descriptions and diagnostic analysis. The signature generation process transforms simple user reports into structured diagnostic data by mapping described issues to failed components and product functionality, preserving reporting simplicity while enabling reliable diagnosis.
4Measurement precision
If detailed diagnostic data is collected from each user, then root cause analysis can be performed, but it is difficult to know whether solutions can be applied to other users' problems
Solution Approach 1:
The system creates standardized problem signature copies that can be replicated across different user contexts. By generating structured signatures from detailed diagnostic data and storing them in a database, the system enables efficient copying and comparison of problem patterns across users, determining whether solutions developed for one user can be applied to others with matching signatures.
Data Source
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AI summary
A problem with a computing system is detected, a root cause is identified and a solution is also identified. Diagnostic data is obtained and an issue signature is generated that maps the issue to failed components and product functionality.