AI-Driven Network Configuration Generation and Validation

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Solution Overview

Problem

The complexity of edge network environments with numerous hardware, firmware, and software configurations leads to a high number of potential configuration combinations, making validation expensive and time-consuming, and introduces risks of implementing non-validated configurations.

Innovation Solution

The use of artificial intelligence (AI) techniques, specifically neural network models, to autonomously generate and validate 'best known configurations' (BKCs) for edge network environments, optimizing fleet topologies and automatically testing new configurations considering operational constraints.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual validation of all configuration combinations is performed, then configuration reliability is improved, but validation time and cost increase significantly

Engineering Contradiction:

Improveconfiguration validation reliabilityVSAvoidvalidation time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system enables configurations to validate themselves through automated self-testing mechanisms. The edge device automatically tests configuration combinations using AI-generated test cases and validates its own state without requiring extensive manual intervention, thereby reducing validation time while maintaining reliability.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system performs preliminary validation by generating AI test cases and validating configurations in advance before deployment. The AI model predicts potential configuration issues and prepares validation test cases beforehand, allowing configurations to be pre-validated and reducing the time required during actual deployment.

Inventive Principle:

Principle #10Preliminary action

2Reliability

If comprehensive configuration validation is performed, then configuration reliability is improved, but validation cost increases

Engineering Contradiction:

Improveconfiguration validation reliabilityVSAvoidvalidation cost

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The system dynamically adjusts validation parameters such as test case selection criteria, validation depth, and resource allocation based on configuration risk profiles. The AI model analyzes configuration characteristics and modifies validation parameters accordingly, performing more rigorous validation only when necessary, thereby reducing overall validation costs while maintaining reliability.

Inventive Principle:

Principle #35Parameter changes

3Reliability

If all potential configuration combinations are validated, then configuration safety is improved, but device complexity increases

Engineering Contradiction:

Improveconfiguration safetyVSAvoidvalidation system complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system extracts and isolates the validation functionality into a separate AI-driven module that operates independently from the core configuration management system. The AI model generates test cases and validates configurations externally, reducing the complexity burden on the main device while ensuring comprehensive validation safety.

Inventive Principle:

Principle #2Taking out (Extraction)

4Productivity

If AI autonomous generation is implemented, then productivity is improved, but measurement precision of configuration validation decreases

Engineering Contradiction:

Improveconfiguration generation productivityVSAvoidconfiguration validation precision

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system implements continuous feedback loops where AI-generated configurations are validated against ground truth data, historical configuration performance, and operational metrics. The AI model learns from validation results and refines its generation accuracy over time, maintaining high productivity while improving measurement precision through iterative feedback from actual system performance data.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250068438A1Autonomous generation of network and device configurations

Publication Date: 2025.02.27 INTEL CORP

AI summary

Described herein are technique to enable the autonomous generation of configurations for a network environment, including but not limited to an edge network of a datacenter. Additional embodiments include prompt-based generation of network and device configurations and neural network based systems for adaptive network management.