Intelligent Network Layout Conversion for Multi-Cloud Deployment
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Solution Overview
Problem
Designing and deploying computer networks in complex environments with diverse cloud and device types is challenging due to the multitude of resources, configuration options, and the scarcity of skilled cloud-native professionals, with existing tools lacking design assistance and automated deployment capabilities.
Innovation Solution
A system and techniques for intelligent network design that convert manual network designs into digital format using computer vision and machine learning, generating provider-independent code, providing design suggestions, and automating deployment across various cloud platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual network design methods are used, then design flexibility is maintained, but design speed and intelligence are reduced
Solution Approach 1:
The patent replaces manual mechanical design processes with computer vision and machine learning systems. The system automatically captures network design diagrams, extracts component information, and generates deployment configurations without human intervention, thereby increasing both design speed and automation level simultaneously.
Solution Approach 2:
The system enables self-service by allowing the network design diagram itself to provide the necessary information for automated processing. The computer vision system extracts data directly from the visual diagram, and the machine learning model automatically generates deployment configurations, making the design process self-sufficient and highly automated.
2Manufacturing precision
If provider-specific code is generated for each cloud platform, then deployment precision is improved, but device complexity increases
Solution Approach 1:
The patent implements a universal machine learning model that can generate provider-specific deployment configurations for multiple cloud platforms (AWS, Azure, GCP, Alibaba) simultaneously. The system maintains a single unified model that adapts to different providers, avoiding the need for separate complex systems for each platform while achieving high deployment precision.
Solution Approach 2:
The system changes parameters dynamically based on the target cloud provider. The machine learning model adjusts its output configurations according to the specific requirements of each provider without changing its core structure, thereby maintaining low system complexity while achieving provider-specific deployment precision.
3Adaptability or versatility
If comprehensive network design options are provided, then adaptability is improved, but ease of operation deteriorates
Solution Approach 1:
The system provides intelligent feedback by analyzing the network design diagram and automatically suggesting optimal configurations and components. The machine learning model learns from the diagram structure and provides targeted recommendations, reducing the need for users to manually explore numerous options while maintaining design flexibility.
Solution Approach 2:
The system performs preliminary actions by pre-processing the network design diagram, automatically extracting component information, and pre-generating deployment configurations before user interaction. This preliminary processing reduces the user's operational effort while maintaining comprehensive adaptability through the generated options.
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.