Reinforcement Learning Root Cause Analysis Vector Matching
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Solution Overview
Problem
In complex computer systems, administrators face difficulties in identifying the root cause of errors and assigning the appropriate subject matter expert (SME) due to the system's hierarchical structure and the specialization of SMEs.
Innovation Solution
A reinforcement learning approach is employed to generate vector representations of the root cause of errors and SMEs based on the hierarchical topology, allowing for the selection of the most suitable SME and diagnostic software to address the issue effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If computer systems become more complex with hierarchical structures, then system functionality and capabilities improve, but the difficulty of identifying root cause of failures increases
Solution Approach 1:
The patent segments the complex computer system into a hierarchical structure with multiple levels (e.g., system level, subsystem level, component level). Each level is analyzed separately to identify errors, and the analysis results are propagated through the hierarchy to determine root causes. This segmentation allows administrators to focus on specific levels rather than analyzing the entire complex system at once.
Solution Approach 2:
The patent introduces an intermediary error analysis system that acts as a mediator between the complex computer system and the administrator. This system automatically analyzes error messages, traces error propagation through the hierarchical structure, and identifies root causes, thereby reducing the difficulty of detection without requiring the administrator to directly analyze the complex system.
2Adaptability or versatility
If computer systems become more complex with hierarchical structures, then system functionality improves, but the time required to identify and resolve errors increases
Solution Approach 1:
The patent performs preliminary analysis by pre-establishing the hierarchical structure of the computer system and pre-configuring error analysis rules for each level. When an error occurs, the system can immediately begin analysis without requiring time to understand the system structure or determine analysis methods, thereby reducing error resolution time while maintaining complex functionality.
Solution Approach 2:
The patent implements a feedback mechanism where error analysis results from one level are automatically used to guide analysis at other levels. The system traces error propagation feedback through the hierarchy, allowing administrators to quickly identify root causes by following the feedback loop rather than manually analyzing each component, thus reducing overall error resolution time.
3Reliability
If the number of subject matter experts increases to handle specialized errors, then error resolution capability improves, but the difficulty of assigning the appropriate SME increases
Solution Approach 1:
The patent creates a universal error analysis system that can handle multiple types of errors across different hierarchical levels through a single integrated platform. This universal system automatically matches error characteristics with appropriate SME expertise areas, eliminating the need for separate assignment processes for different error types and reducing the complexity of managing multiple specialized experts.
Solution Approach 2:
The patent changes the parameters of SME assignment by using automated matching based on error characteristics, expertise profiles, and hierarchical level parameters. Instead of manual assignment based on complex criteria, the system dynamically adjusts assignment parameters algorithmically, making the process scalable to large numbers of SMEs without proportionally increasing assignment complexity.
Data Source
AI summary
Aspects of the invention include generating a vector representation of a root node of the error based on a hierarchical topology of a computing system; generating a respective vector representations of each subject matter expert of a plurality of subject matter experts based at least in part on the hierarchical topology; selecting a subject matter expert based at least in part on the vector representation of root cause of the error; and uploading a diagnostic software to the computing system.