Context Graph Generation for Network Component Relationship Analysis
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Solution Overview
Problem
As networks grow in size and complexity, monitoring and recording relationships between components becomes increasingly difficult, making it challenging to perform timely and adequate analysis of network issues, as existing technologies lack comprehensive tools for displaying relationships between hardware and software components.
Innovation Solution
The generation and augmentation of context graphs that represent relationships between both hardware and software components, using event data and network topology information, to facilitate troubleshooting and root cause analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive monitoring of all component relationships is implemented, then analysis completeness is improved, but system complexity increases
Solution Approach 1:
The system segments the complex network into hierarchical levels (infrastructure, application, data layers) and generates separate context graphs for different failure scenarios. This allows comprehensive monitoring to be divided into manageable segments, each with its own context graph, reducing the overall system complexity while maintaining analysis completeness.
Solution Approach 2:
The patent introduces a new dimension of analysis by creating context graphs that represent relationships in a graphical format rather than traditional tabular data. This dimensional transformation allows the system to handle complex relationships more efficiently and provides intuitive visualization that reduces the perceived complexity of comprehensive monitoring.
2Measurement precision
If detailed relationship data is collected and stored, then root cause analysis accuracy is improved, but data processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-generating context graphs for different failure scenarios before actual failures occur. When a failure happens, the system simply retrieves and displays the pre-prepared context graph matching the failure type, eliminating the need for real-time data processing and significantly reducing analysis time while maintaining high accuracy.
Solution Approach 2:
The context graphs are designed to be dynamic and scenario-specific, allowing the system to quickly switch between different pre-generated graphs based on the type of failure detected. This dynamic approach enables the system to provide accurate root cause analysis for specific scenarios without the overhead of processing all possible data relationships in real-time.
3Productivity
If context graphs are generated for all possible failure scenarios, then troubleshooting effectiveness is improved, but computational resources consumed increases
Solution Approach 1:
The system applies local quality by generating context graphs with specific details relevant to each failure scenario rather than creating comprehensive graphs for all possible scenarios. Each context graph contains only the relationships and components pertinent to its specific failure type, reducing computational resources while maintaining troubleshooting effectiveness for that particular scenario.
Solution Approach 2:
The patent implements partial action by generating context graphs only for the most common and critical failure scenarios rather than attempting to cover every possible failure mode. This selective approach provides sufficient troubleshooting effectiveness for the majority of cases while significantly reducing the computational resources required compared to generating graphs for all possible scenarios.
Data Source
AI summary
As a network increases in size and complexity, it becomes increasingly difficult to monitor and record relationships between components in the network. The lack of knowledge regarding component relationships can make it difficult to adequately and timely perform analysis of network issues or conditions. As a result, automated generation of a context graph that displays relationships among both hardware and software components in a network can help keep pace with a growing network and improve network analysis. The context graph may be generated based, for example, on event data (alternately referred to as event indications) generated by network components and/or event monitoring agents and network topology information. Additionally, the context graph may be augmented to display inter-component relationships based on multi-event correlations. The context graph can be used to assist in troubleshooting network issues or performing root cause analysis.