Bega, DarioGramaglia, MarcoFiore, MarcoBanchs Roca, AlbertCosta-Pérez, Xavier2020-08-112020-08-112020-08-04IEEE INFOCOM 2020 - IEEE Conference on Computer Communications 6-9 July 2020 (Virtual Conference). IEEE, 2020, Pp. 794-803978-1-7281-6412-0https://hdl.handle.net/10016/30757Proceeding of: IEEE INFOCOM 2020 - IEEE Conference on Computer Communications 6-9 July 2020 (Virtual Conference)The combination of network softwarization with network slicing enables the provisioning of very diverse services over the same network infrastructure. However, it also creates a complex environment where the orchestration of network resources cannot be guided by traditional, human-in-the-loop network management approaches. New solutions that perform these tasks automatically and in advance are needed, paving the way to zero-touch network slicing.In this paper, we propose AZTEC, a data-driven framework that effectively allocates capacity to individual slices by adopting an original multi-timescale forecasting model. Hinging on a combination of Deep Learning architectures and a traditional optimization algorithm, AZTEC anticipates resource assignments that minimize the comprehensive management costs induced by resource overprovisioning, instantiation and reconfiguration, as well as by denied traffic demands.Experiments with real-world mobile data traffic show that AZTEC dynamically adapts to traffic fluctuations, and largely outperforms state-of-the-art solutions for network resource orchestration.10eng© 2020 IEEE.Resource managementNetwork slicingForecastingEconomicsElectronic mailPredictive modelsCloud computingconference paperTelecomunicacioneshttps://doi.org/10.1109/INFOCOM41043.2020.9155299open access794803IEEE INFOCOM 2020 - IEEE Conference on Computer Communications 6-9 July 2020 (Virtual Conference)CC/0000030207