Machine learning for network automation: overview, architecture, and applications [Invited Tutorial]

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 ch...

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Vydáno v:	Journal of optical communications and networking Ročník 10; číslo 10; s. D126 - D143
Hlavní autoři:	Rafique, Danish, Velasco, Luis
Médium:	Journal Article Publikace
Jazyk:	angličtina
Vydáno:	Piscataway Optica Publishing Group 01.10.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers (IEEE)
Témata:	Analytics Analytics; Artificial intelligence; Autonomousnetworking; Big data; Communication networks;Machine learning; Optical fiber communication; Telemetry Aprenentatge automàtic Artificial intelligence Autonomous networking Big data Communication networks Comunicacions òptiques Data models Digital media Enginyeria de la telecomunicació Informàtica Intel·ligència artificial Machine learning Management Network control Optical amplifiers Optical communication Optical communications Optical fiber communication Optical fiber networks Optical fibers Optimization Prediction algorithms Predictive maintenance Telecomunicació òptica Telemetry Topology optimization Video transmission Àrees temàtiques de la UPC
ISSN:	1943-0620, 1943-0639
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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.
AbstractList	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. 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. Peer Reviewed
Author	Rafique, Danish Velasco, Luis
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SubjectTerms	Analytics Analytics; Artificial intelligence; Autonomousnetworking; Big data; Communication networks;Machine learning; Optical fiber communication; Telemetry Aprenentatge automàtic Artificial intelligence Autonomous networking Big data Communication networks Comunicacions òptiques Data models Digital media Enginyeria de la telecomunicació Informàtica Intel·ligència artificial Machine learning Management Network control Optical amplifiers Optical communication Optical communications Optical fiber communication Optical fiber networks Optical fibers Optimization Prediction algorithms Predictive maintenance Telecomunicació òptica Telemetry Topology optimization Video transmission Àrees temàtiques de la UPC
Title	Machine learning for network automation: overview, architecture, and applications [Invited Tutorial]
URI	https://ieeexplore.ieee.org/document/8501533 https://www.proquest.com/docview/2126463188 https://recercat.cat/handle/2072/337008
Volume	10
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