Machine learning techniques are becoming increasingly popular in the field of networking. It offers promising solutions for network optimization, security, and management. However, the lack of transparency and interpretability in machine learning models poses challenges for understanding and trusting their decisions in critical networking scenarios. Moreover, ensuring safety and reliability is of utmost importance when deploying machine learning in real-world network environments. Control and decision-making algorithms are critical for the operation of networks, hence we believe that the solutions should be safety bounded and interpretable.

Organizing Committee Kamal Singh, University St-Etienne, France Abbas Bradai, University of Poitiers, France Pham Tran Anh Quang, Huawei Technologies, France Antonio Pescapè, University of Napoli Federico II, Italy Claudio Fiandrino, IMDEA Networks Institute, Madrid, Spain Technical Program Committee: Jong-Hyouk Lee, Sangmyung University, South Korea Antonio Montieri, University of Napoli Federico II, Italy Pere Bariet-Ros, University Politècnica de Catalunya, Spain Carla Fabiana Chiasserini, Politecnica di Torino, Italy Francesco Devoti, NEC laboratories, Germany Zhao Xu, NEC Laboratories, Germany Nabil Benamar, Moulay Ismail University, Meknes, Morocco Jérémie Leguay, Huawei Technologies, France Kandaraj Piamrat, Université de Nantes, France Ons Aouedi, Université de Nantes, France Baptiste Jeudy, University St-Etienne, France Cédric Gueguen, IRISA/University of Rennes 1, France Bernard Cousin, IRISA/University of Rennes 1, France

Friday, 8 December 2023 Please click here to see the proceedings and papers 9h30 - 9h40 Welcome message 9h40 - 10h30 Keynote by Prof. Yassine Hadjadj-Aoul, University of Rennes, France The Quest for Safe Deep Reinforcement Learning-driven Network Slicing: Progress, Pitfalls and Potential The quest for the efficient and autonomous placement of network services is crucial to progressing towards a fully automated network, commonly referred to as a “zero-touch network”. In this presentation, we focus on the results of our research into exploiting the potential of deep reinforcement learning strategies in the field of network slicing.