Network Fabric Analysis via Graph Convolution Networks
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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 analysis tool that converts network fabric wiring diagrams into multigraphs, generates feature matrices, and uses Graph Convolution Networks (GCNs) to assign a score representing the design's quality, enabling automated pre-deployment evaluation and classification into categories like GREEN, YELLOW, or RED, facilitating corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual assessment methods are used for network fabric design quality, then expertise and experience are required for accurate evaluation, but the process becomes time-consuming and difficult to scale
Solution Approach 1:
The patent replaces manual expert assessment (mechanical human analysis) with an automated neural network system that processes network fabric designs. The GCN-based model automatically evaluates design quality, redundancy, and potential failure points without requiring human experts, thereby reducing assessment time while maintaining or improving accuracy through consistent application of learned patterns from training data.
Solution Approach 2:
The patent introduces an intermediary automated evaluation system that acts as a mediator between network fabric design and quality assessment. This intermediary neural network model processes design parameters, topology information, and configuration data to generate quality scores and identify potential issues, serving as a bridge that eliminates the need for direct human expert involvement while preserving assessment quality.
2Reliability
If comprehensive design validation is performed after network deployment, then design issues can be identified, but operational expenses increase and problems affect service availability during the validation period
Solution Approach 1:
The patent performs preliminary action by evaluating network fabric design quality before deployment using the trained neural network model. The system analyzes topology, redundancy, and configuration parameters in advance to identify potential single points of failure, insufficient redundancy, or inefficient resource allocation. This pre-deployment validation prevents costly post-deployment corrections and service disruptions while incurring minimal operational expenses during the design phase.
3Measurement precision
If detailed analysis of complex network fabric topologies is conducted manually, then design quality can be assessed, but the complexity of modern network fabrics makes this process infeasible
Solution Approach 1:
The patent replaces manual analysis capabilities with an automated neural network system specifically designed to handle complex network fabric topologies. The GCN-based model processes graph representations of network designs, automatically analyzing node connections, path redundancy, and topology efficiency regardless of scale. This substitution enables precise measurement of design quality even in highly complex modern network fabrics with numerous switches, routers, and interconnections that would be infeasible to analyze manually.
Solution Approach 2:
The patent transforms the complex network fabric design into standardized parameters and features that the neural network can process. The system converts topology information, device configurations, and connection patterns into numerical representations and graph structures, changing the form of data from complex qualitative descriptions to quantifiable parameters. This parameter transformation enables systematic analysis of design quality metrics such as redundancy levels, path diversity, and resource allocation efficiency.
Data Source
AI summary
Network fabric design and analysis is generally a manual process where the wiring diagram is manually created and analyzed. Because of the complexity of modern network fabric designs, it has become increasingly more difficult to manually detect potential issues with a network fabric design. Accordingly, embodiments herein help automate the analysis of network fabric designs. In one or more embodiments, a trained neural network model or network models receive as input a wiring diagram and analyzes it. In one or more embodiments, the trained model may generate a real-valued score that represents the quality of the design. In one or more embodiments, the trained neural network may classify a particular issue or issues of the network fabric design.