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With various service types including massive machine-type communication (mMTC) and ultra-reliable low-latency communication (URLLC), fifth generation (5G) networks require advanced resources management strategies. As a method to segment network resources logically, network slicing (NS) addresses the challenges of heterogeneity and scalability prevalent in these networks. Traditional software-defined networking (SDN) technologies, lack the flexibility needed for precise control over network resources and fine-grained packet management. This has led to significant developments in programmable switches, with programming protocol-independent packet processors (P4) emerging as a transformative programming language. P4 endows network devices with flexibility and programmability, overcoming traditional SDN limitations and enabling more dynamic, precise network slicing implementations. In our work, we leverage the capabilities of P4 to forge a groundbreaking closed-loop architecture that synergizes the programmable data plane with an intelligent control plane. We set up a token bucket-based bandwidth management and traffic isolation mechanism in the data plane, and use the generative diffusion model to generate the key configuration of the strategy in the control plane. Through comprehensive experimentation, we validate the effectiveness of our architecture, underscoring its potential as a significant advancement in 5G network traffic management.
H. Zhang, N. Liu, X. Chu, K. Long, A. H. Aghvami, and V. C. M. Leung, Network slicing based 5G and future mobile networks: Mobility, resource management, and challenges, IEEE Commun. Mag., vol. 55, no. 8, pp. 138–145, 2017.
Y. Wu, H. N. Dai, H. Wang, Z. Xiong, and S. Guo, A survey of intelligent network slicing management for industrial IoT: Integrated approaches for smart transportation, smart energy, and smart factory, IEEE Commun. Surv. Tutorials, vol. 24, no. 2, pp. 1175–1211, 2022.
S. Zhang, An overview of network slicing for 5G, IEEE Wirel. Commun., vol. 26, no. 3, pp. 111–117, 2019.
Z. Shu and T. Taleb, A novel QoS framework for network slicing in 5G and beyond networks based on SDN and NFV, IEEE Netw., vol. 34, no. 3, pp. 256–263, 2020.
S. Y. Wang, H. W. Hu, and Y. B. Lin, Design and implementation of TCP-friendly meters in P4 switches, IEEE/ACM Trans. Netw., vol. 28, no. 4, pp. 1885–1898, 2020.
Y. W. Chen, C. Y. Li, C. C. Tseng, and M. Z. Hu, P4-TINS: P4-driven traffic isolation for network slicing with bandwidth guarantee and management, IEEE Trans. Netw. Serv. Manag., vol. 19, no. 3, pp. 3290–3303, 2022.
J. Ho, A. Jain, and P. Abbeel, Denoising diffusion probabilistic models, Advances in neural information processing systems, vol. 33, pp. 6840–6851, 2020.
S. Jošilo and G. Dán, Joint wireless and edge computing resource management with dynamic network slice selection, IEEE/ACM Trans. Netw., vol. 30, no. 4, pp. 1865–1878.
A. Thantharate and C. Beard, ADAPTIVE6G: Adaptive resource management for network slicing architectures in current 5G and future 6G systems, J. Netw. Syst. Manag., vol. 31, no. 1, pp. 9, 2022.
T. Mai, H. Yao, N. Zhang, W. He, D. Guo, and M. Guizani, Transfer reinforcement learning aided distributed network slicing optimization in industrial IoT, IEEE Trans. Ind. Inf., vol. 18, no. 6, pp. 4308–4316, 2022.
H. Du, R. Zhang, D. Niyato, J. Kang, Z. Xiong, D. I. Kim, X. S. Shen, and H. V. Poor, Exploring collaborative distributed diffusion-based AI-generated content (AIGC) in wireless networks, IEEE Netw., pp. 1–8, 2024.
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