Bega, DarioGramaglia, MarcoFiore, MarcoBanchs Roca, AlbertCosta-Pérez, Xavier2019-05-072019-05-072019-09-23IEEE INFOCOM 2019- IEEE Conference on Computer Communications, 29 April-2 May 2019, Paris, France [proceedings], 6 pp.https://hdl.handle.net/10016/28332Proceeding of: The 2nd International Workshop on Network Intelligence (NI 2019): Machine Learning for Networking (part of 2019 IEEE International Conference on Computer Communications (IEEE INFOCOM 2019)), 29 April, 2019, Paris, FranceOrchestrating resources in 5G and beyond-5G systems will be substantially more complex than it used to be in previous generations of mobile networks. In order to take full advantage of the unprecedented possibilities for dynamic reconfiguration offered by network softwarization and virtualization technologies, operators have to embed intelligence in network resource orchestrators. We advocate that the automated, data-driven decisions taken by orchestrators must be guided by considerations on the cost that such decisions involve for the operator. We show that such a strategy can be implemented via a deep learning architecture that forecasts capacity rather than plain traffic, thanks to a novel loss function named alfa-OMC. We investigate the convergence properties of alfa-OMC, and provide preliminary results on the performance of the learning process in case studies with real-world mobile network traffic.6eng© 2019 IEEE.5G networksDeep learningMobile networksconference proceedingsTelecomunicacioneshttps://doi.org/10.1109/INFCOMW.2019.8845178open access16CC/0000029120