Machine learning for network automation: Overview, architecture, and applications [invited tutorial]
Cita com:
hdl:2117/125214
Tipus de documentArticle
Data publicació2018
EditorInstitute of Electrical and Electronics Engineers (IEEE)
Condicions d'accésAccés obert
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reproducció, distribució, comunicació pública o transformació sense l'autorització del titular dels drets
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
Networks are complex interacting systems involving cloud operations, core and metro transport, and mobile connectivity all the way to video streaming and similar user applications. With localized and highly engineered operational tools, it is typical of these networks to take days to weeks for any changes, upgrades, or service deployments to take effect. Machine learning, a sub-domain of artificial intelligence, is highly suitable for complex system representation. In this tutorial paper, we review several machine learning concepts tailored to the optical networking industry and discuss algorithm choices, data and model management strategies, and integration into existing network control and management tools. We then describe four networking case studies in detail, covering predictive maintenance, virtual network topology management, capacity optimization, and optical spectral analysis.
CitacióRafique, D., Velasco, L. Machine learning for network automation: Overview, architecture, and applications [invited tutorial]. "Journal of optical communications and networking", 2018, vol. 10, núm. 10, p. D126-D143.
ISSN1943-0620
Versió de l'editorhttps://www.osapublishing.org/jocn/abstract.cfm?URI=jocn-10-10-D126
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jocn-10-10-D126.pdf | Versió publicada pel l'editor. En accés obert a OSA Publishing. | 3,547Mb | Visualitza/Obre |