Search Results for author: Pere Barlet-Ros

Found 11 papers, 5 papers with code

Scaling Graph-based Deep Learning models to larger networks

no code implementations4 Oct 2021 Miquel Ferriol-Galmés, José Suárez-Varela, Krzysztof Rusek, Pere Barlet-Ros, Albert Cabellos-Aparicio

Graph Neural Networks (GNN) have shown a strong potential to be integrated into commercial products for network control and management.

ENERO: Efficient Real-Time Routing Optimization

no code implementations22 Sep 2021 Paul Almasan, Shihan Xiao, Xiangle Cheng, Xiang Shi, Pere Barlet-Ros, Albert Cabellos-Aparicio

To enable the deployment of emergent network applications (e. g., Vehicular networks, Internet of Things), existing Traffic Engineering (TE) solutions must be able to achieve high performance real-time network operation.

Is Machine Learning Ready for Traffic Engineering Optimization?

no code implementations3 Sep 2021 Guillermo Bernárdez, José Suárez-Varela, Albert López, Bo Wu, Shihan Xiao, Xiangle Cheng, Pere Barlet-Ros, Albert Cabellos-Aparicio

In our evaluation, we compare our MARL+GNN system with DEFO, a network optimizer based on Constraint Programming that represents the state of the art in TE.

Multi-agent Reinforcement Learning

Unveiling the potential of Graph Neural Networks for robust Intrusion Detection

1 code implementation30 Jul 2021 David Pujol-Perich, José Suárez-Varela, Albert Cabellos-Aparicio, Pere Barlet-Ros

To this end, we use a graph representation that keeps flow records and their relationships, and propose a novel Graph Neural Network (GNN) model tailored to process and learn from such graph-structured information.

Network Intrusion Detection

Applying Graph-based Deep Learning To Realistic Network Scenarios

no code implementations13 Oct 2020 Miquel Ferriol-Galmés, José Suárez-Varela, Pere Barlet-Ros, Albert Cabellos-Aparicio

Recent advances in Machine Learning (ML) have shown a great potential to build data-driven solutions for a plethora of network-related problems.

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

1 code implementation16 Oct 2019 Paul Almasan, José Suárez-Varela, Arnau Badia-Sampera, Krzysztof Rusek, Pere Barlet-Ros, Albert Cabellos-Aparicio

However, most of the state-of-the-art DRL-based networking techniques fail to generalize, this means that they can only operate over network topologies seen during training, but not over new topologies.

Decision Making

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

no code implementations3 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.

Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN

3 code implementations23 Jan 2019 Krzysztof Rusek, José Suárez-Varela, Albert Mestres, Pere Barlet-Ros, Albert Cabellos-Aparicio

In the paper we show that our model provides accurate estimates of delay and jitter (worst case $R^2=0. 86$) when testing against topologies, routing and traffic not seen during training.

Networking and Internet Architecture

Knowledge-Defined Networking

1 code implementation20 Jun 2016 Albert Mestres, Alberto Rodriguez-Natal, Josep Carner, Pere Barlet-Ros, Eduard Alarcón, Marc Solé, Victor Muntés, David Meyer, Sharon Barkai, Mike J Hibbett, Giovani Estrada, Khaldun Ma`ruf, Florin Coras, Vina Ermagan, Hugo Latapie, Chris Cassar, John Evans, Fabio Maino, Jean Walrand, Albert Cabellos

In this paper, we explore the reasons for the lack of adoption and posit that the rise of two recent paradigms: Software-Defined Networking (SDN) and Network Analytics (NA), will facilitate the adoption of AI techniques in the context of network operation and control.

Networking and Internet Architecture

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