Real-Time Streaming Media Anomaly Detector

A real-time pattern and anomaly detection system for streaming media logs, particularly focused on CMCD (Common Media Client Data) logs from video players.

Features

• Real-time monitoring of streaming video playback quality
• Anomaly detection based on CMCD metrics
• Detection of buffering issues, quality degradation, network problems
• RESTful API for log submission and configuration
• Socket.io real-time notifications of detected anomalies
• Web interface for visualization and monitoring
• Test data generation for development and demonstration

Technologies Used

• Node.js and TypeScript
• Express.js for API
• Socket.io for real-time communication
• Winston for logging
• CTA-5004 CMCD standard compliance

Getting Started

Prerequisites

• Node.js 18+
• npm or yarn

Installation

1. Clone the repository

git clone https://github.com/coderjun/vse-cmcd-monitor.git
cd vse-cmcd-monitor

2. Install dependencies

npm install

3. Configure environment variables (or use the defaults in .env)

4. Start the development server

npm run dev

Usage

1. Access the web interface at http://localhost:3000
2. Use the "Generate Test Data" button to simulate streaming logs
3. View detected anomalies in real-time
4. Submit custom logs via the API or web interface

API Endpoints

• GET /health - Health check endpoint
• GET /api - API documentation
• POST /api/logs - Submit CMCD logs for analysis
• GET /api/config - Get current anomaly detection configuration
• PUT /api/config - Update anomaly detection configuration
• POST /api/test/generate - Generate test data

CMCD Standard

This project uses the CTA-5004 CMCD (Common Media Client Data) standard for streaming media metrics. The CMCD specification defines a standard set of media player performance metrics to be sent from clients to servers during streaming media playback.

Key CMCD metrics used in this project include:

• br - Encoded bitrate in kbps
• bl - Buffer length in milliseconds
• bs - Buffer starvation flag
• mtp - Measured throughput in kbps
• d - Object duration in milliseconds
• dl - Deadline in milliseconds
• su - Startup flag
• sid - Session ID
• ot - Object type

Anomaly Detection

The system can detect several types of anomalies:

1. Buffering Issues

   • Buffer starvation events
   • Low buffer levels

2. Quality Degradation

   • Significant bitrate drops
   • Rapid bitrate fluctuations

3. Startup Issues

   • Multiple startup events in the same session
   • Long startup delays

4. Network Issues

   • Throughput fluctuations
   • Insufficient bandwidth for selected quality

License

MIT License

Acknowledgements

• CTA-5004 CMCD specification

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Please see CONTRIBUTORS.md for a list of contributors.