59 citations · 123 across the 11 of their papers we have counts for
13 papers
Fast Traffic Engineering by Gradient Descent with Learned Differentiable Routing
Krzysztof Rusek, Paul Almasan, José Suárez-Varela +3
Emerging applications such as the metaverse, telesurgery or cloud computing require increasingly complex operational demands on networks (e.g., ultra-reliable low latency). Likewis…
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…
Accelerating Deep Reinforcement Learning for Digital Twin Network Optimization with Evolutionary Strategies
Carlos Güemes-Palau, Paul Almasan, Shihan Xiao +4
The recent growth of emergent network applications (e.g., satellite networks, vehicular networks) is increasing the complexity of managing modern communication networks. As a resul…
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…
Is Machine Learning Ready for Traffic Engineering Optimization?
Guillermo Bernárdez, José Suárez-Varela, Albert López +5
Traffic Engineering (TE) is a basic building block of the Internet. In this paper, we analyze whether modern Machine Learning (ML) methods are ready to be used for TE optimization.…