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Digital Twin Network Optimization Platform for Infrastructure Cost Reduction and Performance Modeling

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🛰️ NetworkTwin: Advanced Network Digital Twin Platform

🌐 Overview

NetworkTwin is a cutting-edge network simulation and digital twin platform designed to revolutionize network infrastructure modeling, testing, and optimization.

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🚀 Key Features

🌍 Multi-Domain Simulation

  • LEO Satellite Networks
  • Enterprise Networks
  • IoT Topologies
  • Data Center Architectures

🤖 Intelligent Capabilities

  • AI-Powered Predictive Modeling
  • Real-Time Network Dynamics
  • Performance Optimization
  • Risk Assessment

🌈 Architecture Overview

graph TD
    A[User Interface] --> B[API Gateway]
    B --> C{Network Simulator}
    C --> D[LEO Satellite Simulation]
    C --> E[Enterprise Network Simulation]
    C --> F[IoT Network Simulation]
    
    D --> G[Performance Predictor]
    E --> G
    F --> G
    
    G --> H[Optimization Engine]
    H --> I[Recommended Configuration]
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📶 Supported Network Types

1. LEO Satellite Networks

  • Dynamic Constellation Modeling
  • Performance Prediction
  • Reliability Simulation

2. Enterprise Networks

  • Multi-Vendor Router Simulation
  • Topology Design
  • Traffic Modeling

3. IoT Networks

  • Mesh Network Simulation
  • Sensor Connectivity
  • Resource Allocation

🛠️ Technical Stack

Backend

  • 👉 Python 3.10+
  • 🚀 FastAPI
  • 📊 PyTorch
  • 💍 Bayesian Modeling

Frontend

  • ⚛️ React.js
  • 📈 D3.js
  • 🎨 Material UI

🗺️ Roadmap

Phase 1: Foundation (Completed)

  • Basic Network Simulation
  • LEO Satellite Modeling
  • Initial Visualization

Phase 2: Advanced Capabilities (In Progress)

  • GNS3 Integration
  • WebSocket Real-Time Updates
  • Advanced AI Predictive Models
  • Multi-Cloud Support

Phase 3: Enterprise-Grade Features (Planned)

  • Kubernetes Integration
  • Advanced Security Modules
  • Enterprise Scalability
  • Comprehensive Monitoring

🎓 Predictive Modeling Approach

graph LR
    A[Raw Network Data] --> B[Data Preprocessing]
    B --> C[Bayesian Network]
    B --> D[Recurrent Neural Network]
    B --> E[Particle Swarm Optimization]
    
    C --> F{Hybrid Predictive Model}
    D --> F
    E --> F
    
    F --> G[Performance Prediction]
    F --> H[Risk Assessment]
    F --> I[Optimization Recommendations]
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🤝 Contribution

We Welcome:

  • 💻 Network Engineers
  • 👀 Research Scientists
  • 🌎 Cloud Architects
  • 🤖 AI/ML Specialists

📞 Contact

Email: [email protected] GitHub: @nabz0r

📜 License

MIT License - Innovation without Boundaries

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Digital Twin Network Optimization Platform for Infrastructure Cost Reduction and Performance Modeling

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