We have relevant datasets, repositories, frameworks and tools of relevance for research and technology transfer initiatives related to knowledge extraction. This section provides an overview on a selection of them and links to download or contact details.

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  . Below a non-exhaustive list of datasets representative of the research in the Department.

As part of the promotion of the availability of resources, the creation of specific communities in Zenodo has also been promoted, at level of research communities (for instance, MIR and Educational Data Analytics) or MSc programs (for instance, the Master in Sound and Music Computing)



Back [NETWORKS] Datasets for the Evaluation of Virtualized Network Functions Resource Allocation Algorithms


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,” 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,” in IEEE SDN/NFV 2015

Updates for this work available in GitHub: https://github.com/windyswsw/DataForNFVSDNExperiments