A machine learning-based approach for virtual network function modeling

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dc.contributor	Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.contributor	Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica
dc.contributor	Universitat Politècnica de Catalunya. CBA - Sistemes de Comunicacions i Arquitectures de Banda Ampla
dc.contributor	Universitat Politècnica de Catalunya. EPIC - Energy Processing and Integrated Circuits
dc.contributor.author	Mestres Sugrañes, Albert
dc.contributor.author	Alarcón Cot, Eduardo José
dc.contributor.author	Cabellos Aparicio, Alberto
dc.date	2018
dc.identifier.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.
dc.identifier.citation	978-1-5386-4069-2
dc.identifier.citation	10.1109/WCNCW.2018.8369019
dc.identifier.uri	http://hdl.handle.net/2117/123193
dc.language.iso	eng
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation	https://ieeexplore.ieee.org/document/8369019
dc.relation	info:eu-repo/grantAgreement/AEI/2PE/TEC2017-90034-C2-1-R
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors
dc.subject	Wireless LANs
dc.subject	Artificial intelligence
dc.subject	Complex networks
dc.subject	E-learning
dc.subject	Learning algorithms
dc.subject	Network function virtualization
dc.subject	Transfer functions
dc.subject	Wireless telecommunication systems
dc.subject	Application layers
dc.subject	Hypervisor
dc.subject	Input traffic
dc.subject	Recent trends
dc.subject	Transport layers
dc.subject	Virtual networks
dc.subject	Learning systems
dc.subject	Xarxes locals sense fil Wi-Fi
dc.title	A machine learning-based approach for virtual network function modeling
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	info:eu-repo/semantics/conferenceObject
dc.description.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 the characteristics of the input traffic. The traffic is represented by a set of features describing characteristics from the transport layer to the application layer in small time batches. The CPU consumption is observed from the hypervisor and corresponds to the average CPU consumption when the traffic batch is processed. We experimentally demonstrate that we can learn the behavior of different VNF in order to model its CPU consumption. We conclude that the behavior of different VNF can be modeled using ML techniques. © 2018 IEEE.
dc.description.abstract	Peer Reviewed

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