Network Topology Configuration via Image Recognition and Machine Learning
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Solution Overview
Problem
As businesses expand and network complexity increases, network engineers face challenges in accurately depicting and maintaining reliable network topologies, making it difficult to track changes and scale networks efficiently without risking downtime.
Innovation Solution
A system that reverse-engineers network maps into actual or simulated configurations using a topologies database and machine learning algorithms to identify matching or predicted network topologies, translating configurations into standard formats for replication and maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If network topology diagrams are manually maintained and updated, then network engineers can understand network connections, but the complexity of tracking changes and maintaining accuracy increases as networks expand
Solution Approach 1:
The patent replaces manual mechanical processes of drawing and updating topology diagrams with automated image processing and machine learning algorithms. The system automatically captures network configurations, processes them through AI models to generate accurate topology diagrams, and updates them dynamically without manual intervention, thereby maintaining high accuracy while reducing the complexity of tracking changes.
Solution Approach 2:
The system creates accurate visual copies (topology diagrams) of the actual network configuration by processing network data through image generation models. These generated diagrams serve as faithful representations that can be automatically updated when network changes occur, eliminating the need for manual copying and reducing maintenance complexity.
2Reliability
If network configurations are manually updated to reflect topology changes, then network reliability can be maintained, but network downtime increases during updates
Solution Approach 1:
The system performs preliminary actions by continuously monitoring network changes and pre-generating updated topology diagrams and configurations before actual network changes are implemented. This allows configurations to be ready and validated in advance, enabling seamless deployment that minimizes network downtime while maintaining reliability.
Solution Approach 2:
The patent enables continuous operation by implementing automated real-time topology tracking and configuration generation. The system continuously monitors network changes, automatically updates topology diagrams, and pushes configurations without interrupting network operations, thereby maintaining both reliability and continuity of service.
3Loss of information
If detailed network topology diagrams are maintained for every network change, then accurate network representation is achieved, but the time and resources required to update diagrams increase
Solution Approach 1:
The system replaces manual diagramming processes with automated image processing and generation technologies. Network configuration data is automatically processed through machine learning models that generate comprehensive topology diagrams, ensuring complete information capture while dramatically improving update efficiency and reducing resource requirements.
Solution Approach 2:
The system efficiently creates accurate visual copies of network topologies by processing configuration data through automated generation models. These copied representations maintain complete information about network connections and changes while being generated rapidly without manual effort, thus preserving information completeness while enhancing productivity.
Data Source
AI summary
A method of configuring network elements in a design network topology includes receiving an image of the design network topology; attempting to retrieve design data from the received image corresponding to the design network topology; when the design data is retrieved, querying a topologies database using the design data to find a previously determined network topology that substantially matches the design network topology; when the design data is not retrieved, predicting a network topology using an unsupervised machine learning algorithm; identifying configurations for network elements in the matching network topology or in the predicted network topology in a configurations database; determining design configurations for the network elements of the design network topology from the identified configurations; translating the design configurations of the network elements to a standard format; and pushing the translated design configurations to actual network elements and/or virtual network elements corresponding to the network elements.