Ontology-Based CLI Knowledge Model for Network Management
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Solution Overview
Problem
Network management systems (NMS) face challenges in maintaining parity with new network equipment and firmware, requiring tedious and expensive integration of device support, with limited impact from existing protocols like SNMP, and lacking standardization in CLI command behaviors and syntax, leading to inefficient configuration management and increased operational expenses.
Innovation Solution
Implementing an ontology-based CLI knowledge model that captures and processes CLI usage and behavior using natural language processing, allowing for the generation of management user interfaces at runtime and enabling NMS to configure multiple devices from a single screen, while tracking security permissions and reducing development time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional NMS integration methods are used for new network devices, then device support can be maintained, but integration becomes tedious, time-consuming, and expensive
Solution Approach 1:
The NMS automatically discovers and integrates new network devices through self-service mechanisms. The system autonomously extracts CLI knowledge from device interactions, builds ontology models, and generates UI components without requiring manual developer intervention for each new device type.
Solution Approach 2:
The system creates reusable templates and ontology models that can be copied and adapted for new device types. Once a device's CLI behavior is analyzed and modeled, the resulting knowledge model serves as a template that can be applied to similar devices, reducing integration time for future devices.
2Adaptability or versatility
If device-specific CLI commands are supported, then comprehensive device control is achieved, but lack of standardization increases complexity
Solution Approach 1:
The ontology-based CLI knowledge model serves multiple functions: it parses device-specific commands, generates standardized UI components, enables configuration management, and provides documentation. This universal model handles diverse CLI syntaxes from different vendors through a common framework.
Solution Approach 2:
The CLI knowledge ontology acts as an intermediary layer between the diverse device-specific CLIs and the NMS. It translates various vendor command structures into a standardized internal representation, allowing the NMS to manage different devices through a unified interface without direct complexity exposure.
3Reliability
If NMS development starts from zero for each new device, then complete device support is achieved, but development time and costs increase significantly
Solution Approach 1:
The system performs preliminary CLI analysis and knowledge extraction automatically when a new device is detected. By pre-building the ontology model and UI templates before full deployment, the system eliminates the need for developers to start from zero, significantly accelerating the integration process.
Solution Approach 2:
The system continuously learns from device interactions and refines its CLI knowledge models. Feedback from actual device responses, configuration operations, and user interactions improves the accuracy and completeness of the ontology, enabling better automatic support for new device variants without additional development effort.
Data Source
AI summary
Systems and methods are disclosed that may be implemented for network management system (NMS) configuration management support for network devices using a learning and natural language processing application to capture the usage and behavior of the Command Line Interface (CLI) of a network device with the aid of a CLI knowledge model, which in one example may be ontology-based.