Computer Vision Network Provisioning from Architecture Diagrams
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Solution Overview
Problem
Existing computing resource provisioning methods are cumbersome, inefficient, and time-consuming, particularly in cloud environments, requiring manual input of complex requirements and multi-step processes.
Innovation Solution
Utilizing computer vision to analyze diagrams of network architectures, detect and recognize visual elements representing computing resources, and generate configuration files to automate the provisioning process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual provisioning processes are used, then provisioning can be performed with existing tools, but the process becomes time-consuming and inefficient
Solution Approach 1:
The patent replaces manual mechanical provisioning operations with computer vision technology. The system captures images of architecture diagrams, uses computer vision algorithms to automatically detect and interpret visual elements representing computing resources, and generates provisioning configurations automatically. This substitution eliminates manual clicking, form-filling, and configuration steps, directly resolving the contradiction by maintaining existing tool compatibility while dramatically reducing provisioning time through automated visual recognition.
Solution Approach 2:
The patent creates a visual copy of the architecture diagram that can be captured as an image. Instead of requiring users to manually recreate architecture designs in complex provisioning interfaces, the system accepts visual copies (images) of the desired architecture and automatically interprets them to generate provisioning configurations. This copying approach maintains simplicity while enabling automated processing, thereby improving productivity without increasing provisioning time.
2Extent of automation
If automated provisioning is implemented, then provisioning speed improves, but system complexity increases
Solution Approach 1:
The patent implements a universal computer vision-based provisioning system that can handle multiple architecture types, computing resource configurations, and provisioning scenarios through a single unified interface. The system accepts various image formats and architecture diagram styles, automatically detects visual elements representing different computing resources (virtual machines, networks, storage), and generates appropriate provisioning configurations. This multi-functionality reduces system complexity by consolidating multiple specialized tools into one automated system, thereby increasing automation extent without proportionally increasing complexity.
Solution Approach 2:
The provisioning system performs self-service by automatically capturing images of architecture diagrams, detecting visual elements, interpreting architecture relationships, and generating provisioning configurations without human intervention. The computer vision system autonomously navigates the provisioning process, eliminating the need for complex manual orchestration and reducing system complexity while maximizing automation. The system serves itself by converting visual inputs directly into actionable provisioning configurations.
3Manufacturing precision
If detailed manual configuration is required, then provisioning accuracy improves, but the number of steps and complexity increase
Solution Approach 1:
The patent replaces manual configuration steps with computer vision-based automatic detection and interpretation. The system captures images of architecture diagrams and uses computer vision algorithms to automatically detect visual elements representing computing resources, their relationships, and configuration parameters. This substitution maintains provisioning accuracy by precisely interpreting visual information while eliminating the need for multiple manual configuration steps, thereby reducing process complexity without sacrificing precision.
Solution Approach 2:
The patent performs preliminary action by capturing the complete architecture diagram as an image before any provisioning operations begin. The computer vision system analyzes the entire architecture in advance, detects all visual elements and their relationships, and generates comprehensive provisioning configurations in one automated process. This preliminary visual capture approach ensures accuracy by preserving the complete architecture design while reducing complexity by eliminating subsequent manual configuration steps.
Data Source
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AI summary
The embodiments of the present disclosure are generally directed to systems and methods for provisioning computing resources using computer vision. An image comprising a plurality of visual elements can be received. The visual elements within the image can be recognized as objects that represent network elements, where the recognized objects represent network elements of a network element type and the network element types comprise one or more of a database, a load balancer, a sub-network, or a virtual machine instance, and an arrangement of the recognized objects that represents a network architecture is recognized. Computing resources can be caused to be provisioned corresponding to the recognized objects by provisioning network elements that comprise one or more of the network element types, wherein the provisioning comprises generating a network architecture for the provisioned network elements based on the recognized arrangement.