RT Conference Proceedings
T1 AZTEC: anticipatory capacity allocation for zero-touch network slicing
A1 Bega, Dario
A1 Gramaglia, Marco
A1 Fiore, Marco
A1 Banchs Roca, Albert
A1 Costa-Pérez, Xavier
AB 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.
PB IEEE
SN 978-1-7281-6412-0
YR 2020
FD 2020-08-04
LK https://hdl.handle.net/10016/30757
UL https://hdl.handle.net/10016/30757
LA eng
NO Proceeding of: IEEE INFOCOM 2020 - IEEE Conference on Computer Communications 6-9 July 2020 (Virtual Conference)
NO The work of University Carlos III of Madrid was supported by H2020 5G-TOURS project (grant agreement no. 856950). The work of NEC Laboratories Europe was supported by H2020 5GROWTH project (grant agreement no. 856709). The research of M. Fiore was partially supported by ANR CANCAN project (ANR-18-CE25-0011).
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