[NETWORKS] Data for NFVSDN experiments
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.
The record for each publication will include access to postprints (following the Open Access policy of the program), as well as datasets and software used. Ongoing work with UPF Library and Informatics will improve the interface and automation of the retrieval of this information soon.
The MdM Strategic Research Program has its own community in Zenodo for material available in this repository 
as well as at the UPF e-repository 
[NETWORKS] Data for NFVSDN experiments
Rankothge, Windhya; Le, Franck; Russo, Alessandra; Lobo, Jorge. Data for NFVSDN experiments. 2017
Network Function Virtualization (NFV) proposes to move packet processing from dedicated hardware middle-boxes to software running on commodity servers: virtualized Network Function (NFs) (i.e, Firewall, Proxy, Intrusion Detection System etc.). We have been developing an experimental platform called Network Function Center (NFC) to study issues related to NFV and NFs, assuming that the NFC will deliver virtualized NFs as a service to clients on a subscription basis. Our studies specially focus on dynamic resource allocation for NFs and we have proposed two new resource allocation algorithms based on Genetic Programming (GP) [1] and currently working on another algorithm based on Iterative Local Search. For a more realistic evaluation of these algorithms, testing data is a fundamental component, but unfortunately, public traffic data specifically referring to virtualized NFs chains is not readily available. Therefore, we developed a model to generate the specific data we needed, based on the available general traffic data [2]. 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.
[1] W. Rankothge, J. Ma, F. Le, A. Russo, and J. Lobo, [“Towards making network function virtualization a cloud computing service,”] (http://repositori.upf.edu/handle/10230/26035) in IM 2015.
[2] W. Rankothge, F. Le, A. Russo, and J. Lobo, [“Experimental results on the use of genetic algorithms for scaling virtualized network functions,”] (http://repositori.upf.edu/handle/10230/26036) in IEEE SDN/NFV 2015.