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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Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1943-0620
1943-0639
DOI:10.1364/JOCN.10.00D126