Dynamic Network Ontology for Real-Time Impact Analysis
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Solution Overview
Problem
Conventional network management tools fail to provide real-time understanding of dynamically changing network behaviors and interactions, inadequately analyzing indirect dependencies that can cause performance issues in complex and dynamic communications networks.
Innovation Solution
A data collection agent is deployed across the network to monitor and analyze communication behaviors, generating an ontological description that dynamically updates to reflect actual network states and relationships, enabling the identification of impact on dependent nodes and service groups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional network management tools use static topology diagrams for analysis, then the system structure is simple and easy to understand, but the tools fail to provide real-time understanding of dynamically changing network behaviors
Solution Approach 1:
The patent transforms static topology diagrams into dynamic ontological descriptions that automatically update to reflect current network states. The system continuously monitors network entities and their relationships, updating the ontological model in real-time to capture actual network behavior rather than relying on static design documentation.
Solution Approach 2:
The system implements continuous feedback loops where network monitoring data feeds into the ontological description, which is then used to generate impact analyses that feed back to administrators. This closed-loop approach enables real-time understanding of network behaviors and their impacts.
2Measurement precision
If conventional tools analyze only direct dependencies between network entities, then the analysis is simple and fast, but they fail to identify indirect dependencies that cause performance issues
Solution Approach 1:
The patent segments the dependency analysis into multiple levels: direct dependencies, indirect dependencies, and transitive dependencies. The ontological description captures relationships at different depths, allowing the system to analyze indirect dependencies without overwhelming computational complexity by breaking down the analysis into manageable segments.
Solution Approach 2:
The system adds a temporal dimension to dependency analysis by continuously updating the ontological description with current network states. This enables the system to identify indirect dependencies that manifest over time, transforming static single-level analysis into dynamic multi-level analysis.
3Loss of information
If the system dynamically updates ontological descriptions to reflect actual network states, then real-time impact analysis is enabled, but the processing complexity and data requirements increase
Solution Approach 1:
The system extracts only the essential and relevant information from complex network monitoring data to update the ontological description. By selectively extracting critical state changes and relationships rather than processing all available data, the system maintains information accuracy while reducing processing complexity.
Solution Approach 2:
The ontological description uses parameter-based representations of network entities and relationships that can be efficiently updated. When network states change, only the affected parameters are modified rather than reprocessing entire data structures, reducing the complexity of dynamic updates.
4Measurement precision
If the system monitors and analyzes all network entity communications, then complete visibility of network behavior is achieved, but the computational resources and processing time increase
Solution Approach 1:
The system performs preliminary actions by continuously maintaining an up-to-date ontological description of network entities and relationships before incidents occur. This pre-computed knowledge base enables rapid impact analysis when issues arise, as the foundational structure is already in place and only requires incremental updates rather than complete analysis from scratch.
Data Source
AI summary
A primary application comprising one or more executables is defined, and a network ontology for the primary application is determined and stored in a database, where the network ontology comprises one or more nodes of an enterprise network that communicate during execution of the one or more executables. Next, a change of state for at least one of the nodes is detected and used to determine one or more elements of the network ontology for the primary application that have a changed state. Further, an impact summary view is generated to indicate the elements of the primary application that have a changed state, and the impact summary view is displayed to a user.