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System Architecture Overview

Introduction​

EyeNet is a comprehensive network monitoring and management system designed to provide real-time insights into network performance, security, and resource utilization. This document outlines the high-level architecture and key components of the system.

System Components​

Frontend (Next.js)​

  • Modern React-based UI with server-side rendering
  • Real-time dashboard with network metrics
  • Interactive network topology visualization
  • Department and user management interfaces

Backend (Node.js)​

  • RESTful API endpoints for data access
  • WebSocket connections for real-time updates
  • Authentication and authorization
  • Data aggregation and processing

Network Controllers​

  • OpenDaylight integration for SDN capabilities
  • pfSense integration for firewall management
  • MikroTik RouterOS integration for network device control
  • Failover mechanisms for high availability

Machine Learning Service​

  • Anomaly detection for network traffic
  • Bandwidth usage prediction
  • Traffic classification
  • Resource optimization recommendations

Database (MongoDB)​

  • Time-series data for network metrics
  • User and department information
  • Device configurations
  • Historical performance data

Key Features​

  1. Real-time Monitoring

    • Network performance metrics
    • Device status and health
    • Bandwidth utilization
    • Application usage statistics

  2. Network Management

    • Device configuration
    • QoS policy management
    • Bandwidth allocation
    • Access control

  3. Analytics and Reporting

    • Historical data analysis
    • Custom report generation
    • Performance trending
    • Capacity planning

  4. Security

    • Anomaly detection
    • Access logging
    • Authentication
    • Authorization

Data Flow​

  1. Data Collection

    • Network devices send metrics
    • Controllers gather device status
    • Applications report usage
    • User interactions logged

  2. Processing

    • Data aggregation
    • ML model inference
    • Alert generation
    • Report compilation

  3. Storage

    • Time-series metrics
    • Configuration data
    • User data
    • Analytics results

  4. Presentation

    • Dashboard updates
    • Alert notifications
    • Report generation
    • API responses

Deployment Architecture​

Development Environment​

  • Local development servers
  • Mock data generators
  • Testing frameworks
  • Development tools

Production Environment​

  • Load balancers
  • Multiple application instances
  • Database clusters
  • Monitoring and logging

Staging Environment​

  • Production-like setup
  • Integration testing
  • Performance testing
  • Security testing