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A machine learning-based approach for virtual network function modeling

Author
Mestres, A.; Alarcon, E.; Albert Cabellos-Aparicio
Type of activity
Presentation of work at congresses
Name of edition
2018 IEEE Wireless Communications and Networking Conference Workshops
Date of publication
2018
Presentation's date
2018-04-15
Book of congress proceedings
2018 IEEE Wireless Communications and Networking Conference Workshops: (WCNCW 2018) Barcelona, Spain 15-18 April 2018
First page
237
Last page
241
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
DOI
https://doi.org/10.1109/WCNCW.2018.8369019 Open in new window
Project funding
Architecting a knowLedge-defined 5G-enabLed network Infrastructure towArd the upcomiNg digital soCiEty
Repository
http://hdl.handle.net/2117/123193 Open in new window
URL
https://ieeexplore.ieee.org/document/8369019 Open in new window
Abstract
Recent trends in networking are proposing the use of Machine Learning (ML) techniques for the control and operation of the network. The application of ML to networking brings several use-cases as well as challenges. The objective of this paper is to explore the feasibility of applying different models and ML techniques to model complex networks elements, such as Virtual Network Functions (VNFs). In particular, we focus on the characterization of the CPU consumption of the VNF as a function of th...
Citation
Mestres, A., Alarcon, E., A. C. A machine learning-based approach for virtual network function modeling. A: IEEE Wireless Communications and Networking Conference Workshops. "2018 IEEE Wireless Communications and Networking Conference Workshops: (WCNCW 2018) Barcelona, Spain 15-18 April 2018". Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 237-241.
Keywords
Application layers, Artificial intelligence, Complex networks, E-learning, Hypervisor, Input traffic, Learning algorithms, Learning systems, Network function virtualization, Recent trends, Transfer functions, Transport layers, Virtual networks, Wireless telecommunication systems
Group of research
CBA - Communications and Broadband Architectures Lab
EPIC - Energy Processing and Integrated Circuits
PERC-UPC - Power Electronics Research Centre

Participants