363 citations · 2.1k across the 64 of their papers we have counts for
11 papers · 1 filter
Graph Neural Networks for Communication Networks: Context, Use Cases and Opportunities
José Suárez-Varela, Paul Almasan, Miquel Ferriol-Galmés +8
Graph neural networks (GNN) have shown outstanding applications in many fields where data is fundamentally represented as graphs (e.g., chemistry, biology, recommendation systems).…
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…
Wide Area Network Autoscaling for Cloud Applications
Berta Serracanta, Jordi Paillisse, Albert Cabellos +4
Modern cloud orchestrators like Kubernetes provide a versatile and robust way to host applications at scale. One of their key features is autoscaling, which automatically adjusts c…
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.…
ENERO: Efficient Real-Time WAN Routing Optimization with Deep Reinforcement Learning
Paul Almasan, Shihan Xiao, Xiangle Cheng +3
Wide Area Networks (WAN) are a key infrastructure in today's society. During the last years, WANs have seen a considerable increase in network's traffic and network applications, i…
IGNNITION: Bridging the Gap Between Graph Neural Networks and Networking Systems
David Pujol-Perich, José Suárez-Varela, Miquel Ferriol +4
Recent years have seen the vast potential of Graph Neural Networks (GNN) in many fields where data is structured as graphs (e.g., chemistry, recommender systems). In particular, GN…