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

Rankothge, Windhya; Le, Franck; Russo, Alessandra; Lobo, Jorge

doi:http://dx.doi.org/10.1109/NFV-SDN.2015.7387405

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

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.2015.7387405

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.

DOI

http://dx.doi.org/10.1109/NFV-SDN.2015.7387405

Related Publication/Data

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

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