Network Configuration Normalization for Accurate Knowledge Graphs
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Solution Overview
Problem
Conventional manual processes for managing network equipment configurations, inventory, and visualization are time-consuming, error-prone, and inefficient, leading to inconsistent knowledge graphs, outdated information, and suboptimal network visualizations.
Innovation Solution
A network management tool leveraging large language models, automation, and natural language processing to streamline configuration normalization, inventory management, and visualization, utilizing AI prompts to parse diverse device configurations, generate standardized formats, and automate inventory updates and network visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual processes are used for managing network equipment configurations, then flexibility and adaptability are maintained, but time consumption and error rates increase significantly
Solution Approach 1:
The system enables self-service automation where the network management platform automatically executes commands, parses outputs, and updates inventory databases without requiring manual administrator intervention for each device configuration task
Solution Approach 2:
Manual mechanical processes (copying configurations to notepads, manual parsing) are replaced with automated software agents that execute commands via API, parse outputs using structured templates, and update databases programmatically
2Measurement precision
If manual knowledge graph creation is performed, then customization and accuracy can be achieved, but consistency and reliability deteriorate due to human error
Solution Approach 1:
Manual knowledge graph creation is replaced with automated AI agents that extract entities and relationships from device configurations using consistent parsing rules and machine learning models, eliminating human variability
Solution Approach 2:
The system implements validation feedback loops where generated knowledge graphs are verified against source configurations, and discrepancies trigger automated corrections or administrator notifications to ensure consistency
3Extent of automation
If conventional network management tools are used, then basic automation is provided, but handling diverse network equipment configurations and natural language interaction remains limited
Solution Approach 1:
The platform provides universal configuration management capabilities that work across multiple network device types and vendors through standardized parsing templates and AI-driven adaptive interpretation, eliminating the need for vendor-specific tools
Solution Approach 2:
The system adapts to diverse equipment by dynamically adjusting parsing parameters and extraction rules based on device type, configuration format, and observed data patterns, enabling flexible handling of varied configurations
4Productivity
If scripting and automation are implemented, then task automation is achieved, but expertise requirements and maintenance complexity increase
Solution Approach 1:
The system performs self-maintenance through automated error handling, logging, and adaptive learning where the platform automatically adjusts to configuration format changes and recovers from execution errors without requiring script modifications
5Ease of operation
If manual inventory updates are performed, then detailed control is maintained, but information currency and management efficiency deteriorate
Solution Approach 1:
The system implements continuous automated inventory updates by periodically executing discovery commands and parsing device configurations to maintain current asset information without requiring manual inventory audits
6Ease of operation
If Mermaid.js visualization is performed manually, then customization is possible, but time consumption and visualization quality decrease
Solution Approach 1:
Manual network visualization creation is replaced with automated generation where the system parses device configurations, builds network topology models, and renders Mermaid.js diagrams programmatically from the extracted data
Data Source
AI summary
Aspects of the subject disclosure may include, for example, obtaining configuration data indicative of a configuration of a component of a communications network; generating a prompt (based upon the configuration data), wherein the prompt is configured for input to a large language model (LLM); responsive to input of the prompt to the LLM, receiving a knowledge graph that was generated by the LLM; validating the knowledge graph relative to the configuration data (resulting in feedback data); responsive to one or more discrepancies existing between the knowledge graph and the first configuration data, generating an updated prompt (based upon the feedback data), wherein the updated prompt is configured for input to the LLM; responsive to input of the updated prompt to the LLM, receiving an updated knowledge graph that was generated by the LLM; and outputting the updated knowledge graph. Other embodiments are disclosed.