Machine Learning for Computer Networking



    Seminar 2 SWS



Beschreibung (EN)

    Artificial intelligence and machine learning (ML) have shown amazing success in various application fields like for instance speech recognition, computer vision and data analytics in general. Popular applications use artificial neural networks, Markov models, Bayesian networks and similar methods. Recently, it has been investigated whether computer networking can benefit from these promising technologies as well.

    A recent overview about the topic is:
    Mowei Wang, Yong Cui, Xin Wang, Shihan Xiao, and Junchen Jiang:
    Machine Learning for Networking: Workflow, Advances and Opportunities.
    IEEE Network, Volume: 32, Issue: 2, March-April 2018.

    A comprehensive survey:
    Raouf Boutaba, Mohammad A. Salahuddin, Noura Limam, Sara Ayoubi, Nashid Shahriar, Felipe Estrada-Solano, and Oscar M. Caicedo:
    A Comprehensive Survey on Machine Learning for Networking: Evolution, Applications and Research Opportunities.
    Journal of Internet Services and Applications, 2018.

    In this seminar participants will give talks about the most relevant machine learning methods and how they can be applied for computer networking problems. All talks will be based on state-of-the-art research papers and should be in English language. A written report can be either in German or English language.

Possible topics are

  • Introduction to Machine Learning Techniques (Date: 2018-11-15) PDF
  • Traffic Classification (moved to undefined date)
  • Traffic Prediction (Date 2018-12-06)PDF
  • Traffic Routing (moved to 2018-12-06)PDF
  • Congestion Control (Date 2018-12-13)PDF
  • Resource Management / Scheduling (Date 2019-01-10)
  • QoS and QoE Management (moved to 2019-01-17)
  • Security / Intrusion Detection (moved to 2019-01-10)
  • Vehicular Networks Overview and Congestion Control as an Example (Date 2019-01-17)
  • Resource Management in Vehicular Networks (Date 2019-01-24)

For any questions please send a mail to Jörg Deutschmann


    WPF, IuK-BA
    WPF, IuK-MA

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