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Empowering Digital Twin for Future Networks with Graph Neural Networks: Overview, Enabling Technologies, Challenges, and Opportunities

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  • Duc-Thinh Ngo

    (Orange Innovation, 35510 Cesson-Sévigné, France
    IMT Atlantique, Nantes University, École Centrale Nantes, CNRS, INRIA, LS2N, UMR 6004, 44000 Nantes, France)

  • Ons Aouedi

    (IMT Atlantique, Nantes University, École Centrale Nantes, CNRS, INRIA, LS2N, UMR 6004, 44000 Nantes, France)

  • Kandaraj Piamrat

    (IMT Atlantique, Nantes University, École Centrale Nantes, CNRS, INRIA, LS2N, UMR 6004, 44000 Nantes, France)

  • Thomas Hassan

    (Orange Innovation, 35510 Cesson-Sévigné, France)

  • Philippe Raipin-Parvédy

    (Orange Innovation, 35510 Cesson-Sévigné, France)

Abstract

As the complexity and scale of modern networks continue to grow, the need for efficient, secure management, and optimization becomes increasingly vital. Digital twin (DT) technology has emerged as a promising approach to address these challenges by providing a virtual representation of the physical network, enabling analysis, diagnosis, emulation, and control. The emergence of Software-defined network (SDN) has facilitated a holistic view of the network topology, enabling the use of Graph neural network (GNN) as a data-driven technique to solve diverse problems in future networks. This survey explores the intersection of GNNs and Network digital twins (NDTs), providing an overview of their applications, enabling technologies, challenges, and opportunities. We discuss how GNNs and NDTs can be leveraged to improve network performance, optimize routing, enable network slicing, and enhance security in future networks. Additionally, we highlight certain advantages of incorporating GNNs into NDTs and present two case studies. Finally, we address the key challenges and promising directions in the field, aiming to inspire further advancements and foster innovation in GNN-based NDTs for future networks.

Suggested Citation

  • Duc-Thinh Ngo & Ons Aouedi & Kandaraj Piamrat & Thomas Hassan & Philippe Raipin-Parvédy, 2023. "Empowering Digital Twin for Future Networks with Graph Neural Networks: Overview, Enabling Technologies, Challenges, and Opportunities," Future Internet, MDPI, vol. 15(12), pages 1-33, November.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:12:p:377-:d:1287461
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    References listed on IDEAS

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    1. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
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