Neural Network Analysis of Network Fabric Design Quality
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Modern network fabric designs are complex and difficult to assess for quality, reliability, efficiency, and expandability before deployment, often leading to issues such as single points of failure or inefficient resource utilization, which can only be identified after implementation.
Innovation Solution
A neural network-based system that analyzes network fabric designs by converting wiring diagrams into multigraphs, generating adjacency and degree matrices, and creating feature matrices to predict the quality of the design, providing a score and identifying potential issues before deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual, rule-based analysis is used to assess network fabric design quality, then the analysis can be performed with simple tools, but the process is time-consuming and cannot scale effectively
Solution Approach 1:
The patent replaces manual, rule-based analysis mechanisms with an automated neural network system. The neural network model processes wiring diagrams and generates quality assessments automatically, eliminating the need for manual evaluation while providing scalable analysis capability.
Solution Approach 2:
The system enables self-service analysis where the neural network autonomously evaluates network fabric designs without requiring manual intervention. The automated system performs quality assessment, identifies issues, and provides recommendations independently, improving productivity while maintaining manageable complexity through standardized processing.
2Reliability
If comprehensive quality assessment is performed manually, then detailed analysis of reliability, efficiency, and expandability can be conducted, but the process becomes time-consuming and impractical for large networks
Solution Approach 1:
The neural network performs comprehensive quality assessment in advance during the design phase, before the network is deployed. By conducting thorough analysis of reliability, efficiency, and expandability beforehand, the system identifies issues early when correction is most effective, eliminating the need for time-consuming post-implementation reviews.
Solution Approach 2:
The patent substitutes manual comprehensive analysis with an automated neural network that can rapidly evaluate multiple aspects of network quality simultaneously. This mechanical substitution enables detailed assessment of reliability, efficiency, and expandability without the time penalty associated with manual review processes.
3Productivity
If neural network-based automated analysis is implemented, then analysis speed and scalability improve, but the system complexity and computational requirements increase
Solution Approach 1:
The patent introduces an intermediary layer in the form of a neural network model that sits between the wiring diagram input and the quality assessment output. This intermediary processes the complex relationships in network fabrics automatically, enabling high throughput analysis while managing system complexity through the standardized neural network architecture.
Solution Approach 2:
The system uses a trained neural network model that captures complex patterns from previously analyzed networks. By copying the knowledge embedded in the trained model, the system can rapidly assess new network designs without requiring complex real-time computations, thus improving throughput while managing architectural complexity.
4Measurement precision
If detailed feature extraction and matrix generation are performed, then the accuracy of quality prediction improves, but the processing time and computational resources increase
Solution Approach 1:
The patent transforms network fabric characteristics into specific mathematical parameters through feature extraction and matrix generation. By converting complex network properties into standardized parameters (adjacency matrices, degree matrices, feature vectors), the system achieves high prediction accuracy while enabling efficient processing through the structured parameter representation that the neural network can process systematically.
Data Source
AI summary
A design of an infrastructure deployment is used for new deployments or as part of an addition to an existing deployment. Because of the complexity of modern infrastructure deployment designs, these designs are subject to a number of problems that are too complex and extensive for manual detection of potential issues. Furthermore, these infrastructure deployments are often critical infrastructure, which means their timely deployment and proper functioning are necessary. While improper design can cause functional problems, selection of an infrastructure element that suffers a supply chain delay so as to delay deployment can be as negatively impactful as a poor design. Accordingly, embodiments herein help automate the analysis of an infrastructure deployment design. In one or more embodiments, a trained neural network receives as input a design and analyzes it to classify a particular issue or issues of the design.