The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Machine Learning for Network Automation: Overview, Architecture, and Applications [Invited Tutorial]
Open accessAbstract
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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Cites
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
A Survey of 5G Network: Architecture and Emerging Technologies
Gupta, A., & Jha, R. K. (2015) · IEEE Access · doi:10.1109/access.2015.2461602 · Cited by 2,420
Cited by
Hybrid SDN evolution: A comprehensive survey of the state-of-the-art
Khorsandroo, S., Sánchez, A. G., Tosun, A. S., Arco, J., & Doriguzzi-Corin, R. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.107981 · Cited by 111
Metadata
- Authors
- Rafique, D., & Velasco, L.
- Venue
- Journal of Optical Communications and Networking
- Publisher
- Optica Publishing Group
- Published
- 2018-08-23
- Volume
- 10
- Issue
- 10
- Pages
- D126
- ISSN
- 1943-0620 (ISSN-L)
- 1943-0639
- OpenAlex
- W2888353605
- Cited by
- 298
- References
- 2
- FWCI
- 23.81
- Type
- journal article
- Language
- en
- Open access
- green
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