The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Machine Learning for Networking: Workflow, Advances and Opportunities
Open accessAbstract
Recently, machine learning has been used in every possible field to leverage its amazing power. For a long time, the networking and distributed computing system is the key infrastructure to provide efficient computational resources for machine learning. Networking itself can also benefit from this promising technology. This article focuses on the application of MLN, which can not only help solve the intractable old network questions but also stimulate new network applications. In this article, we summarize the basic workflow to explain how to apply machine learning technology in the networking domain. Then we provide a selective survey of the latest representative advances with explanations of their design principles and benefits. These advances are divided into several network design objectives and the detailed information of how they perform in each step of MLN workflow is presented. Finally, we shed light on the new opportunities in networking design and community building of this new inter-discipline. Our goal is to provide a broad research guideline on networking with machine learning to help motivate researchers to develop innovative algorithms, standards and frameworks.
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Cites
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Cited by
Toward 6G Networks: Use Cases and Technologies
Giordani, M., Polese, M., Mezzavilla, M., Rangan, S., & Zorzi, M. (2020) · IEEE Communications Magazine · doi:10.1109/mcom.001.1900411 · Cited by 2,241
Deep Learning in Mobile and Wireless Networking: A Survey
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Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018) · Journal of Internet Services and Applications · doi:10.1186/s13174-018-0087-2 · Cited by 1,036
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A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective
Xu, H., Yu, W., Griffith, D., & Golmie, N. (2018) · IEEE Access · doi:10.1109/access.2018.2884906 · Cited by 637
Metadata
- Authors
- Wang, M., Cui, Y., Wang, X., Xiao, S., & Jiang, J.
- Venue
- IEEE Network
- Published
- 2018-03
- Volume
- 32
- Issue
- 2
- Pages
- 92-99
- ISSN
- 0890-8044 (ISSN-L)
- OpenAlex
- W2759910885
- Cited by
- 448
- References
- 14
- FWCI
- 28.62
- Type
- journal article
- Language
- en
- Open access
- green
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