University of Oulu

J. Haavisto, T. Cholez and J. Riekki, "Unleashing GPUs for Network Function Virtualization: an open architecture based on Vulkan and Kubernetes," NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium, 2022, pp. 1-8, doi: 10.1109/NOMS54207.2022.9789822

Unleashing GPUs for Network Function Virtualization : an open architecture based on Vulkan and Kubernetes

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Author: Haavisto, Juuso1,2; Cholez, Thibault3; Riekki, Jukka1
Organizations: 1Center for Ubiquitous Computing, University of Oulu, Oulu, Finland
2University of Oxford, Oxford, United Kingdom
3Université de Lorraine, CNRS, Inria, LORIA, F-54000 Nancy, France
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.4 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2022-06-28


General-purpose computing on graphics processing units (GPGPU) is a promising way to speed up computationally intensive network functions, such as performing real-time traffic classification based on machine learning. Recent studies have focused on integrated graphics units and various performance optimizations to address bottlenecks such as latency. However, these approaches tend to produce architecture-specific binaries and lack the orchestration of functions. A complementary effort would be a GPGPU architecture based on standard and open components, which allows the creation of interoperable and orchestrable network functions. This study describes and evaluates such open architecture based on the cross-platform Vulkan API, in which we execute hand-written SPIR-V code as a network function. We also demonstrate a multi-node orchestration approach for our proposed architecture using Kubernetes. We validate our architecture by executing SPIR-V code performing traffic classification with random forest inference. We test this application both on discrete and integrated graphics cards and on x86 and ARM. We find that in all cases the GPUs are faster than the baseline Cython code.

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Series: IEEE/IFIP Network Operations and Management Symposium
ISSN: 1542-1201
ISSN-E: 2374-9709
ISSN-L: 1542-1201
ISBN: 978-1-6654-0601-7
ISBN Print: 978-1-6654-0602-4
Pages: 1 - 8
DOI: 10.1109/NOMS54207.2022.9789822
Host publication: NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium
Conference: IEEE/IFIP Network Operations and Management Symposium
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Funding: This work is partially funded by the French ANR MOSAICO project, No ANR-19-CE25-0012.
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