Browsing by Author "Suárez-Varela Maciá, José Rafael"
Now showing items 1-20 of 30
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A NetFlow/IPFIX implementation with OpenFlow
(2017)
Conference report
Open Access -
Building a Digital Twin for network optimization using graph neural networks
(2022-11-09)
Article
Open AccessNetwork 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
(Association for Computing Machinery (ACM), 2019)
Conference report
Open AccessToday, 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
(Elsevier, 2022-12-01)
Article
Open AccessDeep 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 contextual network anomalies with graph neural networks
(Association for Computing Machinery (ACM), 2023)
Conference report
Open AccessDetecting anomalies on network traffic is a complex task due to the massive amount of traffic flows in today’s networks, as well as the highly-dynamic nature of traffic over time. In this paper, we propose the use of Graph ... -
Detecting cryptocurrency miners with NetFlow/IPFIX network measurements
(Institute of Electrical and Electronics Engineers (IEEE), 2019)
Conference report
Open AccessIn 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
(Institute of Electrical and Electronics Engineers (IEEE), 2022)
Conference lecture
Open AccessEmerging 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
(Institute of Electrical and Electronics Engineers (IEEE), 2019)
Conference report
Open AccessRecent 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
(2023-05)
Article
Open AccessGraph 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
(2021)
Conference report
Open AccessRecent 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
(2021-11)
Article
Open AccessRecent 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
(Association for Computing Machinery (ACM), 2021)
Conference lecture
Open AccessGraph 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?
(Institute of Electrical and Electronics Engineers (IEEE), 2021)
Conference report
Open AccessTraffic 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
(2023-04)
Article
Open AccessCurrent 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
(2022-11)
Article
Open AccessThe 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
(2021)
Conference report
Open AccessRecent 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
(2021-08-05)
Article
Open AccessRecent 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
(Multidisciplinary Digital Publishing Institute, 2021-09-30)
Article
Open AccessLeaving 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
(Institute of Electrical and Electronics Engineers (IEEE), 2022)
Conference report
Open AccessNetwork 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
(Institute of Electrical and Electronics Engineers (IEEE), 2023-12)
Article
Open AccessNetwork 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 ...
