Now showing items 1-11 of 11

    • A NetFlow/IPFIX implementation with OpenFlow 

      Suárez-Varela Maciá, José Rafael; Barlet Ros, Pere; Carela Español, Valentín (2017)
      Conference report
      Open Access
    • 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) ...
    • 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 ...
    • 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 ...
    • 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 ...
    • Routing based on deep reinforcement learning in optical transport networks 

      Suárez-Varela Maciá, José Rafael; Mestres Sugrañes, Albert; Yu, Junlin; Kuang, Li; Feng, Haoyu; Barlet Ros, Pere; Cabellos Aparicio, Alberto (2019)
      Conference report
      Restricted access - publisher's policy
      This paper addresses the use of Deep Reinforcement Learning for automatic routing in Optical Transport Networks at the electrical-layer level. We propose a DRL-based solution that achieves both high performance and fast learning.
    • SBAR: SDN flow-based monitoring and application recognition 

      Suárez-Varela Maciá, José Rafael; Barlet Ros, Pere (Association for Computing Machinery (ACM), 2018)
      Conference report
      Restricted access - publisher's policy
      We present SBAR, a monitoring system compliant with OpenFlow that provides flow-level measurement reports similar to those of NetFlow in traditional networks, but additionally enriched with labels that classify flows at ...
    • Towards a NetFlow implementation for OpenFlow software-defined networks 

      Suárez-Varela Maciá, José Rafael; Barlet Ros, Pere (Institute of Electrical and Electronics Engineers (IEEE), 2017)
      Conference report
      Open Access
      Obtaining flow-level measurements, similar to those provided by Netflow/IPFIX, with OpenFlow is challenging as it requires the installation of an entry per flow in the flow tables. This approach does not scale well with ...
    • Towards accurate classification of HTTPS traffic in Software-Defined Networks 

      Suárez-Varela Maciá, José Rafael; Barlet Ros, Pere (Institute of Electrical and Electronics Engineers (IEEE), 2018)
      Conference report
      Restricted access - publisher's policy
      In nowadays Internet, there is a strong trend to encrypt the traffic in order to protect users' privacy. This results in a hard challenge for traffic classification, as the payload in the packets cannot be accessed anymore. ...
    • Towards more realistic network models based on Graph Neural Networks 

      Badia Sampera, Arnau; Suárez-Varela Maciá, José Rafael; Almasan Puscas, Felician Paul; Rusek, Krzysztof; Barlet Ros, Pere; Cabellos Aparicio, Alberto (Association for Computing Machinery (ACM), 2019)
      Conference report
      Open Access
      Recently, a Graph Neural Network (GNN) model called RouteNet was proposed as an efficient method to estimate end-to-end network performance metrics such as delay or jitter, given the topology, routing, and traffic of the ...
    • Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN 

      Rusek, Krzysztof; Suárez-Varela Maciá, José Rafael; Mestres Sugrañes, Albert; Barlet Ros, Pere; Cabellos Aparicio, Alberto (Association for Computing Machinery (ACM), 2019)
      Conference report
      Open Access
      Network modeling is a critical component for building self-driving Software-Defined Networks, particularly to find optimal routing schemes that meet the goals set by administrators. However, existing modeling techniques ...