Browsing by Author "Cheng, Xiangle"
Now showing items 1-12 of 12
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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 ... -
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, ... -
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 ... -
FlowDT: A Flow-aware Digital Twin for computer networks
(Institute of Electrical and Electronics Engineers (IEEE), 2022)
Conference report
Open AccessNetwork modeling is an essential tool for network planning and management. It allows network administrators to explore the performance of new protocols, mechanisms, or optimal configurations without the need for testing ... -
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: 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., ... -
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 ... -
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 ...
