115 citations · 235 across the 11 of their papers we have counts for
10 papers · 1 filter
From Simulation to Deep Learning: Survey on Network Performance Modeling Approaches
Carlos Güemes-Palau, Miquel Ferriol-Galmés, Jordi Paillisse-Vilanova +2
Network performance modeling is a field that predates early computer networks and the beginning of the Internet. It aims to predict the traffic performance of packet flows in a giv…
Bridging the Gap Between Simulated and Real Network Data Using Transfer Learning
Carlos Güemes-Palau, Miquel Ferriol-Galmés, Jordi Paillisse-Vilanova +3
Machine Learning (ML)-based network models provide fast and accurate predictions for complex network behaviors but require substantial training data. Collecting such data from real…
RouteNet-Gauss: Hardware-Enhanced Network Modeling with Machine Learning
Carlos Güemes-Palau, Miquel Ferriol-Galmés, Jordi Paillisse-Vilanova +3
Network simulation is pivotal in network modeling, assisting with tasks ranging from capacity planning to performance estimation. Traditional approaches such as Discrete Event Simu…
RouteNet-Fermi: Network Modeling with Graph Neural Networks
Miquel Ferriol-Galmés, Jordi Paillisse, José Suárez-Varela +6
Network models are an essential block of modern networks. For example, they are widely used in network planning and optimization. However, as networks increase in scale and complex…
Network Digital Twin: Context, Enabling Technologies and Opportunities
Paul Almasan, Miquel Ferriol-Galmés, Jordi Paillisse +13
The proliferation of emergent network applications (e.g., telesurgery, metaverse) is increasing the difficulty of managing modern communication networks. These applications entail…
RouteNet-Erlang: A Graph Neural Network for Network Performance Evaluation
Miquel Ferriol-Galmés, Krzysztof Rusek, José Suárez-Varela +4
Network modeling is a fundamental tool in network research, design, and operation. Arguably the most popular method for modeling is Queuing Theory (QT). Its main limitation is that…
Digital Twin Network: Opportunities and Challenges
Paul Almasan, Miquel Ferriol-Galmés, Jordi Paillisse +13
The proliferation of emergent network applications (e.g., AR/VR, telesurgery, real-time communications) is increasing the difficulty of managing modern communication networks. Thes…
Scaling Graph-based Deep Learning models to larger networks
Miquel Ferriol-Galmés, José Suárez-Varela, Krzysztof Rusek +2
Graph Neural Networks (GNN) have shown a strong potential to be integrated into commercial products for network control and management. Early works using GNN have demonstrated an u…
The Graph Neural Networking Challenge: A Worldwide Competition for Education in AI/ML for Networks
José Suárez-Varela, Miquel Ferriol-Galmés, Albert López +21
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 contro…
Applying Graph-based Deep Learning To Realistic Network Scenarios
Miquel Ferriol-Galmés, José Suárez-Varela, Pere Barlet-Ros +1
Recent advances in Machine Learning (ML) have shown a great potential to build data-driven solutions for a plethora of network-related problems. In this context, building fast and…