Rankothge W, Le F, Russo A, Lobo J. Experimental results on the use of genetic algorithms for scaling virtualized network functions. 2015 IEEE Conference on Virtualization and Software Defined Network (NFV-SDN)
List of results published directly linked with the projects co-funded by the Spanish Ministry of Economy and Competitiveness under the María de Maeztu Units of Excellence Program (MDM-2015-0502).
List of publications acknowledging the funding in Scopus.
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Rankothge W, Le F, Russo A, Lobo J. Experimental results on the use of genetic algorithms for scaling virtualized network functions. 2015 IEEE Conference on Virtualization and Software Defined Network (NFV-SDN)
Rankothge W, Le F, Russo A, Lobo J. Experimental results on the use of genetic algorithms for scaling virtualized network functions. 2015 IEEE Conference on Virtualization and Software Defined Network (NFV-SDN)
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.
Additional material:
- Datasets for the Evaluation of Virtualized Network Functions Resource Allocation Algorithms. This repository contains all the details about how we modelled general data into the specific data we wanted, with along the software we used and the assumptions we made during the data modelling process. Using this data and programs, the evaluation results presented in our publications can be easily reproduced.