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

• Published in: Journal of optical communications and networking Vol. 10; no. 10; pp. D126 - D143
• Main Authors: Rafique, Danish, Velasco, Luis
• Format: Journal Article Publication
• Language: English
• Published: Piscataway Optica Publishing Group 01.10.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers (IEEE)
• Subjects: 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
• DOI: 10.1364/JOCN.10.00D126

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.