This workshop is associated with the ERC Consolidator Grant Project FlexNets “Quantifying Flexibility in Communication Networks” that has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program grant agreement No 647158 – FlexNets (2015 – 2020). http://www.networkflexibility.org

Machine learning and artificial intelligence (ML/AI), in particular deep learning, has led to breakthroughs in various domains such as image recognition or natural language processing.This workshop focuses on the topic of ML/AI in the context of communication networks. It aims to discuss research visions and results as well as opportunities and challenges in the intersection of these two areas. The workshop looks for contributions and ideas that provide useful combinations of ML/AI approaches to address networking challenges on all layers from MAC to Application.

• Extended abstracts of up to 2 pages
• PDF format, formatted according to the double-column IEEE format
• The workshop is open to submissions containing preliminary and previously published work

Topics of interest include but are not limited to:

• Data mining & visualization, statistical modeling, and big data analytics for networking data
• Frameworks or tools for data analytics or visualization for networking data
• Time series predictions for networking data such as traffic demands, failures, etc.
• AI/ML algorithms for anomaly detection and attack step prediction in network security
• Protocol design and optimization using ML/AI
• Deep learning and reinforcement learning in network control & management
• Resource allocation for virtualized networks using machine learning
• Machine learning & transfer learning for prediction of networking data & control decisions
• Practical implementations or experience with ML/AI in networking
• Self-learning and adaptive networking protocols and algorithms
• Self-X networks: Self-learning, self-driving, self-repairing, etc.
• New concepts like empowerment for quantifying and improving ML/AI-based concepts

• Andreas Blenk, TUM, andreas.blenk@tum.de
• Stefan Schneider, UPB, stefan.schneider@upb.de
• Robert Bauer, KIT, robert.bauer@kit.edu
• Hauke Heseding, KIT, hauke.heseding@kit.edu

• Oliver Hohlfeld, BTU
• Frank Kargl, Ulm University
• Holger Karl, Paderborn University
• Wolfgang Kellerer, TUM
• Michael Menth, University of Tübingen
• Amr Rizk, Ulm University
• Stefan Schmid, University of Vienna
• Oliver Waldhorst, Hochschule Karlsruhe
• Martina Zitterbart, KIT