Causal Analysis Engine for Root Cause Detection in Complex Systems
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Solution Overview
Problem
Troubleshooting complex systems with interconnected elements is challenging due to the difficulty in determining the root cause of symptoms, especially when elements exist in different system levels or planes, and existing automated tools are insufficient in handling distant or unforeseen root causes and their varied symptoms.
Innovation Solution
A causal analysis engine using a declarative causal language and continuation passing style (CPS) programming to compile causal rules into continuations, which analyzes conditions and determines root causes and their effects across arbitrary complex systems by normalizing system information and applying causal relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional automated tools are used to filter symptoms or correlate with known causes, then analysis speed is improved for common problems, but the ability to detect distant or unforeseen root causes deteriorates
Solution Approach 1:
The system segments the analysis into multiple planes (network plane, computing plane, application plane) and uses plane transition rules to systematically traverse across different system levels. This segmentation allows the system to maintain structured analysis speed while extending detection capability to distant root causes that span multiple planes, overcoming the limitation of traditional single-plane correlation tools.
Solution Approach 2:
The invention adds the dimension of plane transitions to traditional symptom-correlation analysis. Instead of only correlating symptoms within the same system level, the system introduces vertical transitions between planes (e.g., from application plane symptoms to network plane root causes), enabling detection of distant root causes while maintaining analysis efficiency through structured dimensional navigation.
2Ease of operation
If system elements are divided into different planes for organizational convenience, then analysis structure is improved, but the difficulty of analyzing inter-related elements across planes increases
Solution Approach 1:
The system introduces plane transition rules as intermediaries that connect different planes. These rules act as mediators that systematically guide the analysis from one plane to another, reducing the complexity of cross-plane analysis by providing a structured pathway rather than requiring direct complex analysis between all elements across planes.
Solution Approach 2:
The analysis approach becomes dynamic by automatically transitioning between planes based on symptom-root cause relationships. The system adapts its analysis depth and plane traversal based on the specific problem being analyzed, making the cross-plane analysis process flexible and context-dependent rather than statically complex.
3Measurement precision
If manual analysis is performed to trace distant root causes, then detection precision is improved, but analysis time increases significantly
Solution Approach 1:
The system performs preliminary actions by pre-defining plane transition rules and causal relationships between planes. This preliminary structuring of cross-plane dependencies enables the system to automatically trace distant root causes without requiring manual step-by-step analysis, achieving both high detection precision and reduced troubleshooting time through pre-configured analytical pathways.
Solution Approach 2:
The system uses feedback mechanisms to automatically adjust the analysis path based on symptoms observed at each plane. By continuously feedback-ing symptom information and comparing it against plane transition rules, the system can automatically refine its root cause identification, achieving manual-level precision without the time cost of manual analysis.
Data Source
AI summary
A method of performing root cause analysis for use in a system comprising a plurality of inter-related elements, wherein at least some of the elements experience one or more abnormal conditions, comprising defining one or more conditions for more than one element symptomatic of abnormal operation of the element; defining one or more causal relationships between the conditions and results on one or more related elements; compiling the conditions and causal relationships of each element for which conditions and relationships have been defined, as at least one continuation in a continuation passing style (CPS); receiving one or more observed events symptomatic of abnormal operation of at least one element of the system; processing the received events using the conditions and the continuations; concluding at least one root cause of the received events consistent with the received events; and outputting the root cause.