Near real-time estimation of end-to-end performance in converged fixed-mobile networks
10.1016/j.comcom.2019.11.052
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/175328
Tipus de documentArticle
Data publicació2020-01-15
EditorElsevier
Condicions d'accésAccés obert
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
ProjecteMETRO-HAUL - METRO High bandwidth, 5G Application-aware optical network, with edge storage, compUte and low Latency (EC-H2020-761727)
COGNITIVE 5G APPLICATION-AWARE OPTICAL METRO NETWORKS INTEGRATING MONITORING, DATA ANALYTICS AND OPTIMIZATION (AEI-TEC2017-90097-R)
COGNITIVE 5G APPLICATION-AWARE OPTICAL METRO NETWORKS INTEGRATING MONITORING, DATA ANALYTICS AND OPTIMIZATION (AEI-TEC2017-90097-R)
Abstract
The independent operation of mobile and fixed network segments is one of the main barriers that prevents improving network performance while reducing capital expenditures coming from overprovisioning. In particular, a coordinated dynamic network operation of both network segments is essential to guarantee end-to-end Key Performance Indicators (KPI), on which new network services rely on. To achieve such dynamic operation, accurate estimation of end-to-end KPIs is needed to trigger network reconfiguration before performance degrades. In this paper, we present a methodology to achieve an accurate, scalable, and predictive estimation of end-to-end KPIs with sub-second granularity near real-time in converged fixed-mobile networks. Specifically, we extend our CURSA-SQ methodology for mobile network traffic analysis, to enable converged fixed-mobile network operation. CURSA-SQ combines simulation and machine learning fueled with real network monitoring data. Numerical results validate the accuracy, robustness, and usability of the proposed CURSA-SQ methodology for converged fixed-mobile network scenarios.
Descripció
© <2019> Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
CitacióBernal, A. [et al.]. Near real-time estimation of end-to-end performance in converged fixed-mobile networks. "Computer communications", 15 Gener 2020, vol. 150, p. 393-404.
ISSN0140-3664
Versió de l'editorhttps://www.sciencedirect.com/science/article/pii/S014036641930800X
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