Now showing items 1-20 of 29

    • A NetFlow/IPFIX implementation with OpenFlow 

      Suárez-Varela Maciá, José Rafael; Barlet Ros, Pere; Carela Español, Valentín (2017)
      Conference report
      Open Access
    • Building a Digital Twin for network optimization using graph neural networks 

      Ferriol Galmés, Miquel; Suárez-Varela Maciá, José Rafael; Paillissé Vilanova, Jordi; Shi, Xiang; Xiao, Shihan; Cheng, Xiangle; Barlet Ros, Pere; Cabellos Aparicio, Alberto (2022-11-09)
      Article
      Open Access
      Network modeling is a critical component of Quality of Service (QoS) optimization. Current networks implement Service Level Agreements (SLA) by careful configuration of both routing and queue scheduling policies. However, ...
    • Challenging the generalization capabilities of Graph Neural Networks for network modeling 

      Suárez-Varela Maciá, José Rafael; Carol Bosch, Sergi; Rusek, Krzysztof; Almasan Puscas, Felician Paul; Arias Vicente, Marta; Barlet Ros, Pere; Cabellos Aparicio, Alberto (Association for Computing Machinery (ACM), 2019)
      Conference report
      Open Access
      Today, network operators still lack functional network models able to make accurate predictions of end-to-end Key Performance Indicators (e.g., delay or jitter) at limited cost. Recently a novel Graph Neural Network (GNN) ...
    • Deep reinforcement learning meets graph neural networks: Exploring a routing optimization use case 

      Almasan Puscas, Felician Paul; Suárez-Varela Maciá, José Rafael; Rusek, Krzysztof; Barlet Ros, Pere; Cabellos Aparicio, Alberto (Elsevier, 2022-12-01)
      Article
      Restricted access - publisher's policy
      Deep Reinforcement Learning (DRL) has shown a dramatic improvement in decision-making and automated control problems. Consequently, DRL represents a promising technique to efficiently solve many relevant optimization ...
    • Detecting cryptocurrency miners with NetFlow/IPFIX network measurements 

      Zayuelas Muñoz, Jordi; Suárez-Varela Maciá, José Rafael; Barlet Ros, Pere (Institute of Electrical and Electronics Engineers (IEEE), 2019)
      Conference report
      Open Access
      In the last few years, cryptocurrency mining has become more and more important on the Internet activity and nowadays is even having a noticeable impact on the global economy. This has motivated the emergence of a new ...
    • Fast traffic engineering by gradient descent with learned differentiable routing 

      Rusek, Krzysztof; Almasan Puscas, Felician Paul; Suárez-Varela Maciá, José Rafael; Cholda, Piotr; Barlet Ros, Pere; Cabellos Aparicio, Alberto (Institute of Electrical and Electronics Engineers (IEEE), 2022)
      Conference lecture
      Open Access
      Emerging applications such as the metaverse, telesurgery or cloud computing require increasingly complex operational demands on networks (e.g., ultra-reliable low latency). Likewise, the ever-faster traffic dynamics will ...
    • Feature engineering for deep reinforcement learning based routing 

      Suárez-Varela Maciá, José Rafael; Mestres Sugrañes, Albert; Yu, Junlin; Kuang, Li; Feng, Haoyu; Barlet Ros, Pere; Cabellos Aparicio, Alberto (Institute of Electrical and Electronics Engineers (IEEE), 2019)
      Conference report
      Open Access
      Recent advances in Deep Reinforcement Learning (DRL) techniques are providing a dramatic improvement in decision-making and automated control problems. As a result, we are witnessing a growing number of research works that ...
    • Graph neural networks for communication networks: context, use cases and opportunities 

      Suárez-Varela Maciá, José Rafael; Almasan Puscas, Felician Paul; Ferriol Galmés, Miquel; Rusek, Krzysztof; Geyer, Fabien; Cheng, Xiangle; Shi, Xiang; Xiao, Shihan; Scarselli, Franco; Cabellos Aparicio, Alberto; Barlet Ros, Pere (2023-05)
      Article
      Open Access
      Graph neural networks (GNN) have shown outstanding applications in fields where data is essentially represented as graphs (e.g., chemistry, biology, recommendation systems). In this vein, communication networks comprise ...
    • IGNNITION: A framework for fast prototyping of Graph Neural Networks 

      Pujol Perich, David; Suárez-Varela Maciá, José Rafael; Ferriol Galmés, Miquel; Xiao, Shihan; Wu, Bo; Cabellos Aparicio, Alberto; Barlet Ros, Pere (2021)
      Conference report
      Open Access
      Recent years have seen the vast potential of Graph Neural Networks (GNN) in many fields where data is structured as graphs (e.g., chemistry, logistics). However, implementing a GNN prototype is still a cumbersome task that ...
    • IGNNITION: Bridging the gap between graph neural networks and networking systems 

      Pujol Perich, David; Suárez-Varela Maciá, José Rafael; Ferriol Galmés, Miquel; Xiao, Shihan; Wu, Bo; Cabellos Aparicio, Alberto; Barlet Ros, Pere (2021-11)
      Article
      Open Access
      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, GNNs are becoming increasingly popular in ...
    • IGNNITION: fast prototyping of graph neural networks for communication networks 

      Pujol Perich, David; Suárez-Varela Maciá, José Rafael; Ferriol Galmés, Miquel; Wu, Bo; Xiao, Shihan; Cheng, Xiangle; Cabellos Aparicio, Alberto; Barlet Ros, Pere (Association for Computing Machinery (ACM), 2021)
      Conference lecture
      Open Access
      Graph Neural Networks (GNN) have recently exploded in the Machine Learning area as a novel technique for modeling graph-structured data. This makes them especially suitable for applications in the networking field, as ...
    • Is machine learning ready for traffic engineering optimization? 

      Bernárdez Gil, Guillermo; Suárez-Varela Maciá, José Rafael; López Brescó, Albert; Wu, Bo; Xiao, Shihan; Cheng, Xiangle; Barlet Ros, Pere; Cabellos Aparicio, Alberto (Institute of Electrical and Electronics Engineers (IEEE), 2021)
      Conference report
      Open Access
      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. We address this open question through a ...
    • MAGNNETO: A graph neural network-based multi-agent system for traffic engineering 

      Bernárdez Gil, Guillermo; Suárez-Varela Maciá, José Rafael; López Brescó, Albert; Shi, Xiang; Xiao, Shihan; Cheng, Xiangle; Barlet Ros, Pere; Cabellos Aparicio, Alberto (2023-04)
      Article
      Open Access
      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 solutions for Traffic Engineering (TE), which ...
    • Network digital twin: context, enabling technologies, and opportunities 

      Almasan Puscas, Felician Paul; Ferriol Galmés, Miquel; Paillissé Vilanova, Jordi; Suárez-Varela Maciá, José Rafael; Perino, Diego; Lopez, Diego; Pastor Perales, Antonio Agustín; Harvey, Paul; Ciavaglia, Laurent; Wong, Leon; Xiao, Shihan; Ram, Vishnu; Shi, Xiang; Cheng, Xiangle; Cabellos Aparicio, Alberto; Barlet Ros, Pere (2022-11)
      Article
      Open Access
      The proliferation of emergent network applications (e.g., telesurgery, metaverse) is increasing the difficulty of managing modern communication networks. These applications entail stringent network requirements (e.g., ...
    • NetXplain: Real-time explainability of graph neural networks applied to computer networks 

      Pujol Perich, David; Suárez-Varela Maciá, José Rafael; Xiao, Shihan; Wu, Bo; Cabellos Aparicio, Alberto; Barlet Ros, Pere (2021)
      Conference report
      Open Access
      Recent advancements in Deep Learning (DL) have revolutionized the way we can efficiently tackle complex optimization problems. However, existing DL-based solutions are often considered as black boxes due to their high inner ...
    • NetXplain: Real-time explainability of graph neural networks applied to networking 

      Pujol Perich, David; Suárez-Varela Maciá, José Rafael; Xiao, Shihan; Wu, Bo; Cabellos Aparicio, Alberto; Barlet Ros, Pere (2021-08-05)
      Article
      Open Access
      Recent advancements in Deep Learning (DL) have revolutionized the way we can efficiently tackle complex optimization problems. However, existing DL-based solutions are often considered as black boxes with high inner ...
    • Results and achievements of the ALLIANCE Project: New network solutions for 5G and beyond 

      Careglio, Davide; Spadaro, Salvatore; Cabellos Aparicio, Alberto; Lázaro Villa, José Antonio; Barlet Ros, Pere; Gené Bernaus, Joan M.; Perelló Muntan, Jordi; Agraz Bujan, Fernando; Suárez-Varela Maciá, José Rafael; Pagès Raventós, Albert; Paillissé Vilanova, Jordi; Almasan Puscas, Felician Paul; Domingo Pascual, Jordi; Solé Pareta, Josep (Multidisciplinary Digital Publishing Institute, 2021-09-30)
      Article
      Open Access
      Leaving the current 4th generation of mobile communications behind, 5G will represent a disruptive paradigm shift integrating 5G Radio Access Networks (RANs), ultra-high-capacity access/metro/core optical networks, and ...
    • RouteNet-Erlang: A graph neural network for network performance evaluation 

      Ferriol Galmés, Miquel; Rusek, Krzysztof; Suárez-Varela Maciá, José Rafael; Xiao, Shihan; Shi, Xiang; Cheng, Xiangle; Wu, Bo; Barlet Ros, Pere; Cabellos Aparicio, Alberto (Institute of Electrical and Electronics Engineers (IEEE), 2022)
      Conference report
      Open Access
      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 it imposes strong assumptions on the ...
    • RouteNet-Fermi: network modeling with graph neural networks 

      Ferriol Galmés, Miquel; Paillissé Vilanova, Jordi; Suárez-Varela Maciá, José Rafael; Rusek, Krzysztof; Xiao, Shihan; Shi, Xiang; Cheng, Xiangle; Barlet Ros, Pere; Cabellos Aparicio, Alberto (Institute of Electrical and Electronics Engineers (IEEE), 2023-12)
      Article
      Open Access
      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 complexity, some models present limitations, such ...
    • RouteNet: leveraging graph neural networks for network modeling and optimization in SDN 

      Rusek, Krzysztof; Suárez-Varela Maciá, José Rafael; Almasan Puscas, Felician Paul; Barlet Ros, Pere; Cabellos Aparicio, Alberto (2020-06-05)
      Article
      Open Access
      Network modeling is a key enabler to achieve efficient network operation in future self-driving Software-Defined Networks. However, we still lack functional network models able to produce accurate predictions of Key ...