363 citations · 1.6k across the 44 of their papers we have counts for
31 papers · 1 filter
Forecasting Individual NetFlows using a Predictive Masked Graph Autoencoder
Georgios Anyfantis, Pere Barlet-Ros
In this paper, we propose a proof-of-concept Graph Neural Network model that can successfully predict network flow-level traffic (NetFlow) by accurately modelling the graph structu…
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…
Atom: Neural Traffic Compression with Spatio-Temporal Graph Neural Networks
Paul Almasan, Krzysztof Rusek, Shihan Xiao +4
Storing network traffic data is key to efficient network management; however, it is becoming more challenging and costly due to the ever-increasing data transmission rates, traffic…
Building a Graph-based Deep Learning network model from captured traffic traces
Carlos Güemes-Palau, Miquel Ferriol Galmés, Albert Cabellos-Aparicio +1
Currently the state of the art network models are based or depend on Discrete Event Simulation (DES). While DES is highly accurate, it is also computationally costly and cumbersome…
Enhancing 5G Radio Planning with Graph Representations and Deep Learning
Paul Almasan, José Suárez-Varela, Andra Lutu +2
The roll out of new mobile network generations poses hard challenges due to various factors such as cost-benefit tradeoffs, existing infrastructure, and new technology aspects. In…
GraphCC: A Practical Graph Learning-based Approach to Congestion Control in Datacenters
Guillermo Bernárdez, José Suárez-Varela, Xiang Shi +4
Congestion Control (CC) plays a fundamental role in optimizing traffic in Data Center Networks (DCN). Currently, DCNs mainly implement two main CC protocols: DCTCP and DCQCN. Both…
MAGNNETO: A Graph Neural Network-based Multi-Agent system for Traffic Engineering
Guillermo Bernárdez, José Suárez-Varela, Albert López +5
Current trends in networking propose the use of Machine Learning (ML) for a wide variety of network optimization tasks. As such, many efforts have been made to produce ML-based sol…
ASTrack: Automatic Detection and Removal of Web Tracking Code with Minimal Functionality Loss
Ismael Castell-Uroz, Kensuke Fukuda, Pere Barlet-Ros
Recent advances in web technologies make it more difficult than ever to detect and block web tracking systems. In this work, we propose ASTrack, a novel approach to web tracking de…
Leveraging Spatial and Temporal Correlations for Network Traffic Compression
Paul Almasan, Krzysztof Rusek, Shihan Xiao +4
The deployment of modern network applications is increasing the network size and traffic volumes at an unprecedented pace. Storing network-related information (e.g., traffic traces…
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…
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…
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…
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…
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…
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…
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…
Towards Real-Time Routing Optimization with Deep Reinforcement Learning: Open Challenges
Paul Almasan, José Suárez-Varela, Bo Wu +3
The digital transformation is pushing the existing network technologies towards new horizons, enabling new applications (e.g., vehicular networks). As a result, the networking comm…
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…