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Intelligent Caching with Graph Neural Network-Based Deep Reinforcement Learning on SDN-Based ICN

Author

Listed:
  • Jiacheng Hou

    (School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada)

  • Tianhao Tao

    (School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada)

  • Haoye Lu

    (David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada)

  • Amiya Nayak

    (School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada)

Abstract

Information-centric networking (ICN) has gained significant attention due to its in-network caching and named-based routing capabilities. Caching plays a crucial role in managing the increasing network traffic and improving the content delivery efficiency. However, caching faces challenges as routers have limited cache space while the network hosts tens of thousands of items. This paper focuses on enhancing the cache performance by maximizing the cache hit ratio in the context of software-defined networking–ICN (SDN-ICN). We propose a statistical model that generates users’ content preferences, incorporating key elements observed in real-world scenarios. Furthermore, we introduce a graph neural network–double deep Q-network (GNN-DDQN) agent to make caching decisions for each node based on the user request history. Simulation results demonstrate that our caching strategy achieves a cache hit ratio 34.42% higher than the state-of-the-art policy. We also establish the robustness of our approach, consistently outperforming various benchmark strategies.

Suggested Citation

  • Jiacheng Hou & Tianhao Tao & Haoye Lu & Amiya Nayak, 2023. "Intelligent Caching with Graph Neural Network-Based Deep Reinforcement Learning on SDN-Based ICN," Future Internet, MDPI, vol. 15(8), pages 1-20, July.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:8:p:251-:d:1202950
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    References listed on IDEAS

    as
    1. Kao Ge & Jian-Qiang Zhao & Yan-Yong Zhao, 2022. "GR-GNN: Gated Recursion-Based Graph Neural Network Algorithm," Mathematics, MDPI, vol. 10(7), pages 1-13, April.
    2. Qi Lin & Shuo Yu & Ke Sun & Wenhong Zhao & Osama Alfarraj & Amr Tolba & Feng Xia, 2022. "Robust Graph Neural Networks via Ensemble Learning," Mathematics, MDPI, vol. 10(8), pages 1-14, April.
    3. Salahadin Seid Musa & Marco Zennaro & Mulugeta Libsie & Ermanno Pietrosemoli, 2022. "Convergence of Information-Centric Networks and Edge Intelligence for IoV: Challenges and Future Directions," Future Internet, MDPI, vol. 14(7), pages 1-31, June.
    4. 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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