Multi-Fabric Network Design Automation
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Solution Overview
Problem
Designing multi-fabric networks is complex due to factors like resource management across multiple cloud providers, interoperability issues, varying cost structures, security differences, performance variabilities, and regulatory requirements, making it challenging to create effective and efficient network connectivity.
Innovation Solution
An automatic multi-fabric network connectivity generator using machine learning and data analysis to comprehensively consider technical, business, and regulatory factors, representing network fabrics as undirected acyclic graphs and employing generative models to optimize connectivity between network components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual design of multi-fabric networks is performed, then design customization and control are maintained, but design complexity and time consumption increase significantly
Solution Approach 1:
The patent replaces manual mechanical design processes with an automated AI-based system that uses machine learning models to generate network fabric designs. The system processes inputs (requirements, constraints, preferences) and automatically outputs optimized network fabric configurations, eliminating the need for manual analysis and design creation while handling complex multi-fabric scenarios.
Solution Approach 2:
The system enables self-service design by allowing users to input their requirements and constraints, then the AI model automatically generates and optimizes the network fabric design without requiring expert intervention. The design process becomes autonomous, where the system serves itself by generating optimal configurations based on learned patterns from training data.
2Manufacturing precision
If comprehensive consideration of multiple factors (technical, business, regulatory) is performed, then design quality and optimization are improved, but analysis time and processing complexity increase
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models on extensive datasets of network designs before actual design requests are received. During operation, the pre-trained models can quickly generate optimized designs without re-analyzing all factors from scratch, significantly reducing analysis time while maintaining comprehensive consideration of technical, business, and regulatory factors.
Solution Approach 2:
The patent substitutes manual comprehensive analysis with automated AI models that have been trained to consider multiple factors simultaneously. The machine learning models process complex inputs and generate optimized designs much faster than manual analysis, achieving both comprehensive consideration and speed through computational processing rather than human review.
3Measurement precision
If machine learning models are trained on extensive data, then design accuracy and optimization are improved, but training time and computational resources increase
Solution Approach 1:
The system performs preliminary training of machine learning models during an initial phase before actual design work begins. By completing the computationally intensive training process in advance on extensive datasets, the system prepares accurate design models that can operate efficiently during production, avoiding the need to re-train on every new design task and reducing ongoing computational resource consumption.
Data Source
AI summary
Presented herein are embodiments for automatically generating a multi-fabric design. In one or more embodiments, a multi-fabric design generator system comprises a plurality of generative machine learning models that, given a graph specification for a desired multi-fabric network, generates a set of preliminary graphs. The preliminary graphs may be input into an ensemble model that comprises a reinforcement learning module, which may be trained to select the best components from the various models to create a tailored design according to specific design criteria and customer requirements or constraints. Thus, given a set of desired requirements (e.g., latency, resistance to congestion, cost, scale, bijection, etc.), the multi-fabric design generator system generates a multi-fabric design that fulfills that set of requirements; thereby providing the ability to generate customized designs for each customer based on their requirements (e.g., technical, business, and regulatory).