Experimental results on the use of genetic algorithms for scaling virtualized network functions

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• dc.contributor.author Rankothge, Windhyaca
• dc.contributor.author Le, Franckca
• dc.contributor.author Russo, Alessandraca
• dc.contributor.author Lobo, Jorgeca
• dc.date.accessioned 2016-03-31T07:06:02Z
• dc.date.available 2016-03-31T07:06:02Z
• dc.date.issued 2015ca
• dc.description.abstract Network Function Virtualization (NFV) is bringing closer the possibility to truly migrate enterprise data centers into the cloud. However, for a Cloud Service Provider to offer such services, important questions include how and when to scale out/in resources to satisfy dynamic traffic/application demands. In previous work [1], we have proposed a platform called Network Function Center (NFC) to study research issues related to NFV and Network Functions (NFs). In a NFC, we assume NFs to be implemented on virtual machines that can be deployed in any server in the network. In this paper we present further experiments on the use of Genetic Algorithms (GAs) for scaling out/in NFs when the traffic changes dynamically. We combined data from previous empirical analyses [2], [3] to generate NF chains and for getting traffic patterns of a day and run simulations of resource allocation decision making. We have implemented different fitness functions with GA and compared their performance when scaling out/in over time.
• dc.description.sponsorship This research was sponsored by the U.S. Army Research Laboratory and the U.K. Ministry of Defence and was accomplished under Agreement Number W911NF-06-3-0001. The views and conclusions contained in this document are those of the author(s) and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Army Research Laboratory, the U.S. Government, the U.K. Ministry of Defence or the U.K. Government. The U.S. and U.K. Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on. Jorge Lobo was also partially supported by the Secretaria dUniversitats i Recerca de la Generalitat de Catalunya.
• dc.format.mimetype application/pdfca
• dc.identifier.citation Rankothge W, Ma J, Le F, Russo A, Lobo J. Experimental results on the use of genetic algorithms for scaling virtualized network functions. In: 2015 IEEE Conference on Virtualization and Software Defined Network (NFV-SDN); 2015 Nov 18-21; San Francisco, CA. IEEE; 2015. p. 47-53. DOI 10.1109/NFV-SDN.2015.7387405ca
• dc.identifier.doi http://dx.doi.org/10.1109/NFV-SDN.2015.7387405
• dc.identifier.uri http://hdl.handle.net/10230/26036
• dc.language.iso engca
• dc.publisher Institute of Electrical and Electronics Engineers (IEEE)ca
• dc.relation Materials relacionats: Rankothge W, Le F, Russo A, Lobo J. Data modelling for the evaluation of virtualized network functions resource allocation algorithms. 2017. 4 p. http://hdl.handle.net/10230/34517
• dc.relation.ispartof 2015 IEEE Conference on Virtualization and Software Defined Network (NFV-SDN); 2015 Nov 18-21; San Francisco, CA. IEEE; 2015. p. 47-53.
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• dc.rights.accessRights info:eu-repo/semantics/openAccessca
• dc.title Experimental results on the use of genetic algorithms for scaling virtualized network functionsca
• dc.type info:eu-repo/semantics/conferenceObjectca
• dc.type.version info:eu-repo/semantics/acceptedVersionca