Browsing by Author "Almasan Puscas, Felician Paul"
Now showing items 1-15 of 15
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Accelerating deep reinforcement learning for digital twin network optimization with evolutionary strategies
(Institute of Electrical and Electronics Engineers (IEEE), 2022)
Conference report
Open AccessThe recent growth of emergent network applications (e.g., satellite networks, vehicular networks) is increasing the complexity of managing modern communication networks. As a result, the community proposed the Digital Twin ... -
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 ... -
Desenvolupament d'una aplicació que treballa sobre el protocol LISP
(Universitat Politècnica de Catalunya, 2017-04-19)
Bachelor thesis
Open AccessL’increment de les aplicacions que utilitzen la xarxa i dels dispositius connectats a Internet està forçant a buscar una nova forma més òptima i flexible d’encaminar paquets. A conseqüència d’aquest fet, han sorgit nous ... -
ENERO: Efficient real-time WAN routing optimization with Deep Reinforcement Learning
(2022-09-04)
Article
Open AccessWide 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, imposing new requirements on existing network ... -
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 ... -
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 ... -
Leveraging graph neural networks for optimization and traffic compression in network digital twins
(Universitat Politècnica de Catalunya, 2023-07-17)
Doctoral thesis
Open Access(English) In recent years, several industry sectors have adapted the Digital Twin (DT) paradigm to improve the performance of physical systems. This paradigm consists of leveraging computational methods to build high-fidelity ... -
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., ... -
Performance-oriented digital twins for packet and optical networks
(2023-10-23)
Research report
Open AccessThis draft introduces the concept of a Network Digital Twin (NDT), including the architecture as well as the interfaces. Then two specific instances of the NDT are introduced, the first one for packet networks. This ... -
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: leveraging graph neural networks for network modeling and optimization in SDN
(2020-06-05)
Article
Open AccessNetwork 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 ... -
The graph neural networking challenge: a worldwide competition for education in AI/ML for networks
(2021-07-01)
Article
Open AccessDuring 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 control, monitoring and management tasks in ... -
Towards more realistic network models based on Graph Neural Networks
(Association for Computing Machinery (ACM), 2019)
Conference report
Open AccessRecently, 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 ... -
Towards real-time routing optimization with deep reinforcement learning: open challenges
(Institute of Electrical and Electronics Engineers (IEEE), 2021)
Conference report
Open AccessThe digital transformation is pushing the existing network technologies towards new horizons, enabling new applications (e.g., vehicular networks). As a result, the networking community has seen a noticeable increase in ...
