Rankothge W, Ramalhinho , Lobo J. On the Scaling of Virtualized Network Functions. 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM)
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.
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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)
Rankothge W, Ramalhinho , Lobo J. On the Scaling of Virtualized Network Functions. 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM)
Rankothge W, Ramalhinho , Lobo J. On the Scaling of Virtualized Network Functions. 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM)
Offering Virtualized Network Functions (VNFs) as a service requires automation of cloud resource management to allocate cloud resources for the VNFs dynamically. Most of the existing solutions focus only on the initial resource allocation. However, the allocation of resources must adapt to dynamic traffic demands and support fast scaling mechanisms. There are three basic scaling models: vertical where re-scaling is achieved by changing the resources assigned to the VNF in the host server, horizontal where VNFs are replicated or removed to do rescaling, and migration where VNFs are moved to servers with more resources. In this paper, we present an Iterated Local Search (ILS) based framework for automation of resource reallocation that supports the three scaling models. We, then, use the framework to run experiments and compare the different scaling approaches, specifically how the optimization is affected by the scaling approach and the optimization objectives.