Accelerating Deep Reinforcement Learning for Digital Twin Network Optimization with Evolutionary Strategies

1 code implementation • 1 Feb 2022 • Carlos Güemes-Palau, Paul Almasan, Shihan Xiao, Xiangle Cheng, Xiang Shi, Pere Barlet-Ros, Albert Cabellos-Aparicio

In the context of DTN, DRL can be leveraged to solve optimization problems without directly impacting the real-world network behavior.

Management reinforcement-learning +1

Graph Neural Networks for Communication Networks: Context, Use Cases and Opportunities

1 code implementation • 29 Dec 2021 • José Suárez-Varela, Paul Almasan, Miquel Ferriol-Galmés, Krzysztof Rusek, Fabien Geyer, Xiangle Cheng, Xiang Shi, Shihan Xiao, Franco Scarselli, Albert Cabellos-Aparicio, Pere Barlet-Ros

Graph neural networks (GNN) have shown outstanding applications in many fields where data is fundamentally represented as graphs (e. g., chemistry, biology, recommendation systems).

Management Recommendation Systems

ENERO: Efficient Real-Time WAN Routing Optimization with Deep Reinforcement Learning

1 code implementation • 22 Sep 2021 • Paul Almasan, Shihan Xiao, Xiangle Cheng, Xiang Shi, Pere Barlet-Ros, Albert Cabellos-Aparicio

In this paper we propose Enero, an efficient real-time TE solution based on a two-stage optimization process.

reinforcement-learning Reinforcement Learning (RL)

The Graph Neural Networking Challenge: A Worldwide Competition for Education in AI/ML for Networks

1 code implementation • 26 Jul 2021 • José Suárez-Varela, Miquel Ferriol-Galmés, Albert López, Paul Almasan, Guillermo Bernárdez, David Pujol-Perich, Krzysztof Rusek, Loïck Bonniot, Christoph Neumann, François Schnitzler, François Taïani, Martin Happ, Christian Maier, Jia Lei Du, Matthias Herlich, Peter Dorfinger, Nick Vincent Hainke, Stefan Venz, Johannes Wegener, Henrike Wissing, Bo Wu, Shihan Xiao, Pere Barlet-Ros, Albert Cabellos-Aparicio

During the last decade, Machine Learning (ML) has increasingly become a hot topic in the field of Computer Networks and is expected to be gradually adopted for a plethora of control, monitoring and management tasks in real-world deployments.

Management

Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use case

1 code implementation • 16 Oct 2019 • Paul Almasan, José Suárez-Varela, Krzysztof Rusek, Pere Barlet-Ros, Albert Cabellos-Aparicio

GNNs are Deep Learning models inherently designed to generalize over graphs of different sizes and structures.

Decision Making Reinforcement Learning (RL)

RouteNet: Leveraging Graph Neural Networks for network modeling and optimization in SDN

1 code implementation • 3 Oct 2019 • Krzysztof Rusek, José Suárez-Varela, Paul Almasan, Pere Barlet-Ros, Albert Cabellos-Aparicio

Network modeling is a key enabler to achieve efficient network operation in future self-driving Software-Defined Networks.