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Open Access

A P4-Based Approach to Traffic Isolation and Bandwidth Management for 5G Network Slicing

School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
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Abstract

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.

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Tsinghua Science and Technology
Pages 171-185
Cite this article:
He W, Yao H, Chang H, et al. A P4-Based Approach to Traffic Isolation and Bandwidth Management for 5G Network Slicing. Tsinghua Science and Technology, 2025, 30(1): 171-185. https://doi.org/10.26599/TST.2024.9010020

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Received: 02 November 2023
Revised: 25 December 2023
Accepted: 18 January 2024
Published: 11 September 2024
© The Author(s) 2025.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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