Multistage Learning Process for Root Cause Identification
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Solution Overview
Problem
Existing response management systems face challenges in efficiently identifying and remediating undesired operations in managed systems due to resource limitations and the complexity of data analysis.
Innovation Solution
A multistage learning process is employed to analyze job traces and identify a narrowed set of root causes for undesired operations, which are then used to remediate future occurrences, thereby reducing the time and resources required for issue resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional response management systems analyze all job traces to identify root causes, then comprehensive analysis is achieved, but the time and resources required increase significantly
Solution Approach 1:
The patent segments the root cause identification process into multiple stages: first identifying a small set of highly likely root causes (top 1-5%) from job traces, then progressively expanding the search scope if needed. This segmentation allows the system to achieve comprehensive analysis accuracy while limiting the time investment to analyzing only the most probable causes initially.
Solution Approach 2:
The system performs preliminary analysis by pre-identifying and ranking potential root causes before full remediation begins. By预先 analyzing job traces to establish a prioritized list of root causes, the system prepares the information needed for rapid decision-making, reducing the time required when undesired operations occur.
2Reliability
If service agents manually analyze all potential root causes, then thorough investigation is possible, but the complexity of the process increases
Solution Approach 1:
The patent introduces an automated analysis system that acts as an intermediary between the undesired operation and the service agent. This intermediary automatically processes job traces, identifies patterns, ranks root causes by likelihood, and presents a simplified list to agents, thereby reducing the complexity of manual analysis while maintaining thorough investigation capabilities.
Solution Approach 2:
The system replaces the mechanical process of manual analysis with automated computational methods. Algorithms automatically parse job traces, correlate events, and identify root causes, substituting human cognitive effort with machine-based analysis that is both thorough and less complex to execute.
3Loss of information
If comprehensive job trace analysis is performed, then all potential issues are identified, but resource consumption increases
Solution Approach 1:
The patent applies partial action by analyzing only the portion of job traces most likely to contain root causes. Rather than processing every single trace equally, the system focuses computational resources on the top 1-5% of suspected causes, achieving sufficient completeness without the full resource cost of exhaustive analysis.
Solution Approach 2:
The system dynamically adjusts analysis parameters such as the depth of trace exploration, the number of causes to investigate, and the level of detail required. By changing these parameters based on the specific undesired operation and initial findings, the system optimizes resource usage while maintaining identification completeness.
Data Source
AI summary
Methods and systems for managing undesired operation of managed systems are disclosed. To manage the undesired operation, information regarding the undesired operation may be obtained. A multistage learning process may be performed to identify likely root causes for the undesired operation using the obtained information. Remediation processes based on the likely root causes may be performed to reduce the likelihood of the undesired operation from occurring in the future.