Search Results for author: Miquel Ferriol-Galmés

Found 6 papers, 3 papers with code

RouteNet-Fermi: Network Modeling with Graph Neural Networks

2 code implementations22 Dec 2022 Miquel Ferriol-Galmés, Jordi Paillisse, José Suárez-Varela, Krzysztof Rusek, Shihan Xiao, Xiang Shi, Xiangle Cheng, Pere Barlet-Ros, Albert Cabellos-Aparicio

We have tested RouteNet-Fermi in networks of increasing size (up to 300 nodes), including samples with mixed traffic profiles -- e. g., with complex non-Markovian models -- and arbitrary routing and queue scheduling configurations.

Scheduling

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

1 code implementation29 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

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.

BIG-bench Machine Learning Management

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.

Scheduling

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