Causality Mapping Agent for Complex Environment Fault Identification
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Solution Overview
Problem
Existing monitoring tools in complex computing environments fail to account for mutual dependencies between systems and components, making it difficult to pinpoint faults or problems effectively.
Innovation Solution
An automated system that uses statistical tools for causality and correlation tests to measure the strength and direction of causal relations between environment components, including a causality mapping agent, environment monitoring agent, and a causality and/or correlation test library to generate causality graphs for root cause analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing monitoring tools are used to monitor complex computing environments, then monitoring coverage is provided, but the ability to pinpoint faults or problems is insufficient due to failure to account for mutual dependencies
Solution Approach 1:
The patent segments the complex computing environment into individual components and their dependencies, representing them as discrete nodes and edges in a causality graph. This segmentation allows the system to break down the complexity of mutual dependencies into manageable, analyzable units that can be processed individually while maintaining their relational context.
Solution Approach 2:
The patent introduces a causality mapping agent as an intermediary that analyzes environment data and generates causality graphs. This intermediary component bridges the gap between raw monitoring data and fault identification, systematically processing dependency relationships to reveal causal connections that would otherwise be hidden in the complex system.
2Reliability
If statistical tools for causality and correlation tests are implemented to measure causal relations between components, then root cause analysis capability is improved, but computational complexity and processing requirements increase
Solution Approach 1:
The patent performs preliminary actions by continuously collecting and storing environment data in data stores before faults occur. The causality mapping agent pre-computes and maintains causality graphs that capture dependency relationships, so when a fault occurs, the analysis can leverage pre-prepared causal models rather than computing everything from scratch, reducing real-time computational complexity.
Solution Approach 2:
The patent creates simplified copies of the complex computing environment in the form of causality graphs and environment component models. These graphical representations copy the essential dependency relationships without the full complexity of the actual system, allowing statistical tools to analyze causal relations on the simplified model rather than the entire complex system.
3Ease of operation
If causality graphs are generated to show causal relationships between components, then troubleshooting and optimization capability is enhanced, but data processing and graph generation time increase
Solution Approach 1:
The patent implements continuous monitoring and continuous updates of the causality graph as environment data changes. The system maintains an ongoing process of data collection, analysis, and graph updates, so the causality graph is continuously refined and ready for immediate use during troubleshooting, eliminating the need for time-consuming graph generation when faults occur.
Solution Approach 2:
The system performs preliminary actions by pre-generating and maintaining causality graphs based on collected environment data before faults occur. The causality mapping agent continuously processes environment data and updates the graphs in advance, so when troubleshooting is needed, the graphs are already prepared and can be immediately utilized, reducing the time loss during actual troubleshooting operations.
Data Source
AI summary
An automated agent for the causal mapping of complex environments. Specifically, a disclosed method and system entails the application of statistical tools, or causality tests, to measure the strength and direction of causal relations between two or more environment components. Further, the execution of the causality tests may be an offline process that may be triggered periodically to account for changes or updates to an environment over time.