Journal
JOURNAL OF OPTICAL COMMUNICATIONS AND NETWORKING
Volume 10, Issue 10, Pages D126-D143Publisher
OPTICAL SOC AMER
DOI: 10.1364/JOCN.10.00D126
Keywords
Analytics; Artificial intelligence; Autonomous networking; Big data; Communication networks; Machine learning; Optical fiber communication; Telemetry
Categories
Funding
- EU project METRO-HAUL [761727]
- AEI/FEDER TWINS project [TEC2017-90097-R]
- CELTIC EUREKA project SENDATE-Secure-DCI [C2015/3-4]
- German BMBF [16KIS0477K]
- Catalan Institution for Research and Advanced Studies (ICREA)
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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.
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