Causal Analysis Engine Tracing Root Causes Across System Planes
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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 that compiles causal rules into continuations using a declarative causal language, allowing for the determination of root causes and their effects across arbitrary complex systems by relating conditions through causal rules and propagating status changes across different system planes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional automated troubleshooting tools are used, then the analysis process is automated, but the tools are insufficient in handling distant or unforeseen root causes and their varied symptoms across different system planes
Solution Approach 1:
The patent introduces a new dimension of analysis by organizing system elements into multiple planes (network plane, computing plane, application plane) and using causal rules that can traverse across these planes. This multi-planar approach allows the automated tool to consider distant and unforeseen root causes that traditional single-plane tools would miss, thereby improving reliability while maintaining automation.
Solution Approach 2:
The causal analysis engine is designed as a universal tool that can handle multiple types of system elements across different planes using a unified causal rule framework. The engine can analyze various symptom types and propagate causes across different system levels, making it versatile enough to handle diverse troubleshooting scenarios without sacrificing accuracy.
2Measurement precision
If the system is analyzed in detail to determine root causes across different planes, then the accuracy of root cause determination is improved, but the time consumption and complexity of analysis increases
Solution Approach 1:
The patent pre-establishes causal rules that define the relationships between system elements across different planes before actual troubleshooting occurs. These pre-defined causal rules allow the analysis engine to quickly evaluate potential root causes without performing exhaustive real-time analysis, thereby maintaining high precision while reducing time consumption.
Solution Approach 2:
The patent segments the complex system into multiple planes (network, computing, application) and further segments the analysis into discrete causal rules. This segmentation allows the troubleshooting process to focus on specific planes and causal relationships relevant to the reported symptom, avoiding the need to analyze the entire system in detail and thus reducing time while maintaining accuracy.
3Adaptability or versatility
If comprehensive causal rules are compiled into continuations, then the ability to trace symptoms to root causes across different planes is improved, but the device complexity increases
Solution Approach 1:
The patent introduces continuations as an intermediary mechanism that simplifies the execution of comprehensive causal rules. Continuations allow the causal analysis engine to manage complex multi-plane analysis by breaking down the evaluation into manageable sequential steps, thereby reducing the apparent complexity of the engine while maintaining its versatile capability to trace symptoms across different planes.
4Ease of operation
If existing code book rules are used for determining root problems, then the analysis is straightforward, but the same root cause may result in many different symptoms that may not have been anticipated
Solution Approach 1:
The patent implements a dynamic causal rule system where rules are not fixed but can be evaluated and extended based on the specific symptom and system state. The causal analysis engine dynamically evaluates causal rules against actual system conditions and can traverse across multiple planes to discover unforeseen root causes, making the system adaptable to varied symptoms while maintaining ease of operation through automated evaluation.
Data Source
AI summary
A method of compiling causal rules into continuations for use in root cause analysis of a system comprising a plurality of inter-related elements, comprising defining observable events occurring on system elements; defining at least one of a cause and a result of each of the events; defining causal rules, each rule describing a causal relationship between an event and one of its cause and its result; and compiling the causal relationships as continuations in a continuation passing style (CPS) for use in analyzing the root cause of subsequent observed events symptomatic of at least one problem on the system.