Xiangle Cheng

Zhejiang University of Technology

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Shihan Xiao

Huawei Technologies (China)

Albert Cabellos‐Aparicio

Universitat Politècnica de Catalunya

Pere Barlet‐Ros

Universitat Politècnica de Catalunya

José Suárez‐Varela

Telefónica (Spain)

Xiang Shi

Xuzhou Medical College

Miquel Ferriol-Galmés

Consorci Institut D'Investigacions Biomediques August Pi I Sunyer

Paul Almasan

Universitat Politècnica de Catalunya

Krzysztof Rusek

AGH University of Krakow

Huancai Lu

Eastern Institute of Technology, Ningbo

Xiang Shi

Yale University

Cooperative Institutions

Zhejiang University of Technology

Universitat Politècnica de Catalunya

University of Illinois Urbana-Champaign

Peking University

Zhejiang University

Huawei Technologies (China)

Huawei Technologies (United Kingdom)

Telefonica Research and Development

Shandong University

FC Barcelona

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Neural Quantile Optimization for Edge-Cloud Computing

Bin Du He Zhang Xiangle Cheng Lei Zhang

We seek the best traffic allocation scheme for the edge-cloud computing network subject to constraints and burstable billing. First, we formulate a family of quantile-based integer programming problems for a fixed network topology with random parameters describing the traffic demands. Then, to overcome the difficulty caused by the discrete feature, we generalize the Gumbel-softmax reparameterization method to induce an unconstrained continuous optimization problem as a regularized continuation of the discrete problem. Finally, we introduce the Gumbel-softmax sampling neural network to solve optimization problems via unsupervised learning. The neural network structure reflects the edge-cloud computing topology and is trained to minimize the expectation of the cost function for unconstrained continuous optimization problems. The trained network works as an efficient traffic allocation scheme sampler, outperforming the random strategy in feasibility and cost value. Besides testing the quality of the output allocation scheme, we examine the generalization property of the network by increasing the time steps and the number of users. We also feed the solution to existing integer optimization solvers as initial conditions and verify the warm-starts can accelerate the short-time iteration process. The framework is general, and the decoupled feature of the random neural networks is adequate for practical implementations.

Quantile

10.2139/ssrn.4563907

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Principal component analysis based multiplexing solution for MIMO systems in high-speed railway

Xiangle Cheng Xuming Fang

In order to enable reliable spatial multiplexing transmission, an environment with rich scatterers is often required, which is not equipped in most railway running environment. So the MIMO channel with sparse scatterer and dominant LOS component in high-speed railway scenario is usually strongly spatial correlated and often fails to support a multiplexing transmission link. While it is actually not a pure LOS environment even in open plain or on a viaduct according to the existing engineering measurements. Hence, in high-speed railway environment, it is still highly possible to support partial multiplexing transmission if configuring a reasonable antenna placement structure with adequate antennas along the train body in the future. To target this problem, this paper investigates the solution of reconstructing the highly correlated railway channel into an equivalent channel with less correlation using the method of principal component analysis and thereon proposes an optimal partial multiplexing transmission solution to improve the link performance. With the proposed solution, the capacity in the distant area from the base station is verified to be significantly improved and that in close distance area is maintained meanwhile the condition number is reduced to be less than 10 to meet the required link quality.

Spatial multiplexing

Component (thermodynamics)

10.1109/hmwc.2014.7000212

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ENERO: Efficient Real-Time WAN Routing Optimization with Deep Reinforcement Learning

arXiv (Cornell University) (2021)

Paul Almasan Shihan Xiao Xiangle Cheng Xiang Shi Pere Barlet‐Ros

Wide 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 technologies (e.g., low latency and high throughput). Consequently, Internet Service Providers (ISP) are under pressure to ensure the customer's Quality of Service and fulfill Service Level Agreements. Network operators leverage Traffic Engineering (TE) techniques to efficiently manage network's resources. However, WAN's traffic can drastically change during time and the connectivity can be affected due to external factors (e.g., link failures). Therefore, TE solutions must be able to adapt to dynamic scenarios in real-time. In this paper we propose Enero, an efficient real-time TE solution based on a two-stage optimization process. In the first one, Enero leverages Deep Reinforcement Learning (DRL) to optimize the routing configuration by generating a long-term TE strategy. To enable efficient operation over dynamic network scenarios (e.g., when link failures occur), we integrated a Graph Neural Network into the DRL agent. In the second stage, Enero uses a Local Search algorithm to improve DRL's solution without adding computational overhead to the optimization process. The experimental results indicate that Enero is able to operate in real-world dynamic network topologies in 4.5 seconds on average for topologies up to 100 edges.

10.48550/arxiv.2109.10883

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Digital Twin Network: Opportunities and Challenges

arXiv (Cornell University) (2022)

Paul Almasan Miquel Ferriol-Galmés Jordi Paillissé José Suárez‐Varela Diego Perino

The proliferation of emergent network applications (e.g., AR/VR, telesurgery, real-time communications) is increasing the difficulty of managing modern communication networks. These applications typically have stringent requirements (e.g., ultra-low deterministic latency), making it more difficult for network operators to manage their network resources efficiently. In this article, we propose the Digital Twin Network (DTN) as a key enabler for efficient network management in modern networks. We describe the general architecture of the DTN and argue that recent trends in Machine Learning (ML) enable building a DTN that efficiently and accurately mimics real-world networks. In addition, we explore the main ML technologies that enable developing the components of the DTN architecture. Finally, we describe the open challenges that the research community has to address in the upcoming years in order to enable the deployment of the DTN in real-world scenarios.

Enabling

10.48550/arxiv.2201.01144

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Citations (45)

Is Machine Learning Ready for Traffic Engineering Optimization?

arXiv (Cornell University) (2021)

Guillermo Bernárdez José Suárez‐Varela Albert López Bo Wu Shihan Xiao

Traffic 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 comparative analysis between the state of the art in ML and the state of the art in TE. To this end, we first present a novel distributed system for TE that leverages the latest advancements in ML. Our system implements a novel architecture that combines Multi-Agent Reinforcement Learning (MARL) and Graph Neural Networks (GNN) to minimize network congestion. In our evaluation, we compare our MARL+GNN system with DEFO, a network optimizer based on Constraint Programming that represents the state of the art in TE. Our experimental results show that the proposed MARL+GNN solution achieves equivalent performance to DEFO in a wide variety of network scenarios including three real-world network topologies. At the same time, we show that MARL+GNN can achieve significant reductions in execution time (from the scale of minutes with DEFO to a few seconds with our solution).

10.48550/arxiv.2109.01445

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Flow Neural Network for Traffic Flow Modelling in IP Networks

Xiangle Cheng Yuchen He Feifei Long Shihan Xiao Fenglin Li

This paper presents and investigates a novel and timely application domain for deep learning: sub-second traffic flow modelling in IP networks. Traffic flows are the most fundamental components in an IP based networking system. The accurate modelling of the generative patterns of these flows is crucial for many practical network applications. However, the high nonlinearity and dynamics of both the traffic and network conditions make this task challenging, particularly at the time granularity of sub-second. In this paper, we cast this problem as a representation learning task to model the intricate patterns in data traffic according to the IP network structure and working mechanism. Accordingly, we propose a customized Flow Neural Network, which works in a self-supervised way to extract the domain-specific data correlations. We report the state-of-the-art performances on both synthetic and realistic traffic patterns on multiple practical network applications, which provides a good testament to the strength of our approach.

Granularity

Representation

Traffic classification

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ENERO: Efficient Real-Time Routing Optimization

arXiv (Cornell University) (2021)

Paul Almasan Shihan Xiao Xiangle Cheng Xiang Shi Pere Barlet-Ros

Wide 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 as well as in the number of network applications. To enable the deployment of emergent network applications (e.g., Vehicular networks, Internet of Things), existing Traffic Engineering (TE) solutions must be able to achieve high performance real-time network operation. In addition, TE solutions must be able to adapt to dynamic scenarios (e.g., changes in the traffic matrix or topology link failures). However, current TE technologies rely on hand-crafted heuristics or computationally expensive solvers, which are not suitable for highly dynamic TE scenarios. In this paper we propose Enero, an efficient real-time TE engine. Enero is based on a two-stage optimization process. In the first one, it leverages Deep Reinforcement Learning (DRL) to optimize the routing configuration by generating a long-term TE strategy. We integrated a Graph Neural Network (GNN) into the DRL agent to enable efficient TE on dynamic networks. In the second stage, Enero uses a Local Search algorithm to improve DRL's solution without adding computational overhead to the optimization process. Enero offers a lower bound in performance, enabling the network operator to know the worst-case performance of the DRL agent. We believe that the lower bound in performance will lighten the path of deploying DRL-based solutions in real-world network scenarios. The experimental results indicate that Enero is able to operate in real-world dynamic network topologies in 4.5 seconds on average for topologies up to 100 edges.

Heuristics

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Citations (3)

Tailoring circumferential traveling waves in a damped circular plate coupled to a spring-mass oscillator using phased force excitations

Applied Mathematical Modelling (2025)

Xiangle Cheng Huancai Lu Dapeng Tan Hua Xia

10.1016/j.apm.2025.116045

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Neural quantile optimization for edge-cloud networking

Computer Networks (2024)

Bin Du He Zhang Xiangle Cheng Lei Zhang

We seek the best traffic allocation scheme for the edge–cloud networking subject to SD-WAN architecture and burstable billing. First, we formulate a family of quantile-based integer programming problems for a fixed network topology with random parameters describing the traffic demands. Then, to overcome the difficulty caused by the discrete feature, we generalize the Gumbel-softmax reparameterization method to induce an unconstrained continuous optimization problem as a regularized continuation of the discrete problem. Finally, we introduce the Gumbel-softmax sampling neural network to solve optimization problems via unsupervised learning. The neural network structure reflects the edge–cloud networking topology and is trained to minimize the expectation of the cost function for unconstrained continuous optimization problems. The trained network works as an efficient traffic allocation scheme sampler, outperforming the random strategy in feasibility and cost value. Besides testing the quality of the output allocation scheme, we examine the generalization property of the network by increasing the time steps and the number of users. We also feed the solution to existing integer optimization solvers as initial conditions and verify the warm-starts can accelerate the short-time iteration process. The framework is general, and the decoupled feature of the random neural networks is adequate for practical implementations.

Quantile

10.1016/j.comnet.2024.110713

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Citations (0)

One-dimensional granular chains as transmitted force attenuators

Nonlinear Dynamics (2023)

Zhenjiang Zhou D. Michael McFarland Xiangle Cheng Huancai Lu Alexander F. Vakakis

Bead

Chain (unit)

10.1007/s11071-023-08658-6

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Citations (3)