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Hybrid P4 Programmable Pipelines for 5G gNodeB and User Plane Functions
University of Campinas, Brasil.
University of Campinas, Brasil.
Queen Mary University of London, UK.
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).ORCID iD: 0000-0002-9446-8143
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2022 (English)In: IEEE Transactions on Mobile Computing, ISSN 1536-1233, E-ISSN 1558-0660, p. 1-18Article in journal (Refereed) Published
Abstract [en]

This paper focuses on hybrid pipeline designs for User Plane Function and next-generation NodeB leveraging target-specific features and an insightful discussion of P4 and target challenges and limitations. The entire or disaggregated UPF runs on P4 targets and allocates packet processing data paths in P4 hardware or DPDK/x86 software based on flow characteristics (e.g., heavy hitters) and QoS requirements (e.g., low-latency slices). For the hybrid gNodeB, most packet processing is executed in commodity Tofino hardware, while unsupported functions such as Automatic Repeat Request and cryptography are performed in DPDK/x86. We show that our hybrid UPF improves the scalability by 18× and reduces latency up to 50%. The results also suggest that careful traffic allocation to pipeline targets is required to optimize each target's strength and avoid processing delays. Finally, we demonstrate a QoS-oriented application of the hybrid UPF and present gNodeB buffer service benchmarks. 

Place, publisher, year, edition, pages
IEEE, 2022. p. 1-18
Keywords [en]
5G mobile communication systems; Benchmarking; Data handling; Error correction; Field programmable gate arrays (FPGA); Pipeline processing systems; Quality of service; Reconfigurable hardware, 5g; 5g mobile communication; Field programmable gate array; Field programmables; Hardware; Hybrid network; Mobile communications; Programmable gate array; Programmable network; User planes, Pipelines
National Category
Communication Systems Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-92120DOI: 10.1109/TMC.2022.3201512ISI: 001098818300005Scopus ID: 2-s2.0-85137560638OAI: oai:DiVA.org:kau-92120DiVA, id: diva2:1701207
Funder
Knowledge FoundationAvailable from: 2022-10-05 Created: 2022-10-05 Last updated: 2024-07-23Bibliographically approved

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Kassler, Andreas

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