Cloud Network Diagram Conversion for Multi-Provider Deployment
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Solution Overview
Problem
Designing and deploying computer networks in cloud-based environments is complex due to the multitude of resource types and configuration options, lack of design assistance in existing tools, and the scarcity of cloud-native skills, leading to inefficiencies in transitioning from architectural diagrams to code.
Innovation Solution
An intelligent network design system that captures and converts basic network designs into digital format using computer vision and machine learning, generates provider-independent code, and provides design recommendations, enabling rapid deployment and automation across various cloud platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual methods are used to convert architectural diagrams to code, then design flexibility is maintained, but productivity is reduced and time consumption increases
Solution Approach 1:
The system uses optical character recognition (OCR) and image processing techniques to automatically copy and extract information from architectural diagrams, converting visual representations into structured digital data and code without manual transcription. This automated copying process resolves the contradiction by dramatically reducing the time required for diagram-to-code conversion while maintaining design flexibility through editable digital outputs.
Solution Approach 2:
The patent introduces an intermediary processing layer between architectural diagrams and deployment code that automatically translates diagram elements into code structures. This intermediary system uses machine learning models trained on diagram patterns to bridge the gap between visual design and executable code, eliminating manual conversion steps and significantly improving productivity without sacrificing design flexibility.
2Reliability
If cloud provider-specific tools are used for deployment, then deployment accuracy is improved, but adaptability to multiple providers decreases
Solution Approach 1:
The system employs a universal code generation engine that produces provider-agnostic infrastructure as code (IaC) templates capable of deploying to multiple cloud providers. The generated code uses standardized resource definitions and abstraction layers that can be configured for different providers (AWS, Azure, GCP, etc.), maintaining deployment accuracy through provider-specific configuration options while preserving adaptability across diverse cloud environments.
Solution Approach 2:
The patent implements parameter-driven code generation where the same base code template can be adapted to different cloud providers by changing configuration parameters and provider-specific settings. This allows the system to maintain high deployment accuracy for each provider while preserving versatility, as the underlying code structure remains consistent but adapts to provider-specific requirements through parameter modification.
3Adaptability or versatility
If comprehensive design options are provided to users, then design flexibility is improved, but device complexity increases
Solution Approach 1:
The system segments the complex array of design options into organized categories and hierarchical groups based on functional domains (networking, computing, storage, security). This segmentation presents comprehensive design flexibility through structured, manageable interfaces rather than overwhelming users with a flat list of all possible options, reducing perceived complexity while maintaining full adaptability.
Solution Approach 2:
The patent implements preliminary action by providing intelligent defaults and pre-configured templates for common deployment scenarios. Users can quickly deploy using pre-built patterns while retaining the ability to customize any aspect of the design. This approach reduces interface complexity by hiding advanced options until needed, while preserving full design flexibility for users who require customization.
4Productivity
If automated code generation is implemented, then productivity is improved, but manufacturing precision may worsen due to automation errors
Solution Approach 1:
The system incorporates multi-layer feedback mechanisms including syntax validation, semantic checking, and provider-specific rule verification in the code generation process. Automated tests and validation rules check generated code for correctness, consistency with architectural diagrams, and compliance with cloud provider requirements. This feedback loop maintains high code accuracy despite automated generation, preventing errors while preserving productivity benefits.
Solution Approach 2:
The patent implements beforehand cushioning by building in validation, error checking, and correction mechanisms directly into the code generation process. The system anticipates potential errors through pattern matching and rule-based validation, cushioning against accuracy issues before they affect the final output. This proactive error prevention maintains manufacturing precision while preserving the speed benefits of automation.
Data Source
AI summary
A system and techniques for intelligent network design allows for capture and conversion of a basic network design, often manually created, into a digital format without duplication of effort. The disclosed techniques provide a faster, more intelligent approach for designing a network by analyzing many thousands of existing network designs and recommending proposed solutions based on user provided objectives. The techniques can generate provider independent code that provides flexibility in supporting arbitrary provider targets. The techniques can also output provider specific code that allow for rapid, efficient deployment of the network. In addition, the technique can output network system architecture designs of varying details that can be customized for the intended audience. The techniques provide for automated importing and updating provider changes to network components, schemas, and application programming interfaces reducing any lag in system design.