Browsing by Author "Xiao, Shihan"
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Accelerating deep reinforcement learning for digital twin network optimization with evolutionary strategies
Güemes Palau, Carlos; Almasan Puscas, Felician Paul; Xiao, Shihan; Cheng, Xiangle; Shi, Xiang; Barlet Ros, Pere; Cabellos Aparicio, Alberto (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
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 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
Almasan Puscas, Felician Paul; Xiao, Shihan; Cheng, Xiangle; Shi, Xiang; Barlet Ros, Pere; Cabellos Aparicio, Alberto (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
Ferriol Galmés, Miquel; Cheng, Xiangle; Shi, Xiang; Xiao, Shihan; Barlet Ros, Pere; Cabellos Aparicio, Alberto (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
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 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
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 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
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 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
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 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?
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 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
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 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
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 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
Pujol Perich, David; Suárez-Varela Maciá, José Rafael; Xiao, Shihan; Wu, Bo; Cabellos Aparicio, Alberto; Barlet Ros, Pere (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
Pujol Perich, David; Suárez-Varela Maciá, José Rafael; Xiao, Shihan; Wu, Bo; Cabellos Aparicio, Alberto; Barlet Ros, Pere (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 ... -
Performance-oriented digital twins for packet and optical networks
Cabellos Aparicio, Alberto; Janz, Christopher; Almasan Puscas, Felician Paul; Ferriol Galmés, Miquel; Barlet Ros, Pere; Paillissé Vilanova, Jordi; Xiao, Shihan; Shi, Xiang; Cheng, Xiangle; Guo, Aihua; Perino, Diego; Lopez, Diego; Pastor Perales, Antonio Agustín (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
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 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
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 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 ... -
Towards real-time routing optimization with deep reinforcement learning: open challenges
Almasan Puscas, Felician Paul; Suárez-Varela Maciá, José Rafael; Wu, Bo; Xiao, Shihan; Barlet Ros, Pere; Cabellos Aparicio, Alberto (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 ...