Machine Learning Empowered Content Delivery: Status, Challenges, and Opportunities

Zhihui Lu; Keke Gai; Qiang Duan; Kim Kwang Raymond Choo; Junnan Li; Jie Wu; Yajing Xu

doi:10.1109/MNET.011.2000141

Machine Learning Empowered Content Delivery: Status, Challenges, and Opportunities

Zhihui Lu, Keke Gai^*, Qiang Duan, Kim Kwang Raymond Choo, Junnan Li, Jie Wu, Yajing Xu

^*Corresponding author for this work

School of Cyberspace Science and Technology

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

Abstract

Trends such as increasing mobile device ownership and faster and more affordable internet speed have contributed to significant demands in media-based services on mobile devices. There has been an emphasis on content delivery networks to support media-based services. However, achieving high-performance content delivery in large-scale dynamic network environments is still operationally challenging, especially when we have to consider the diverse application requirements. One emerging research trend is to explore the use of machine learning techniques to enhance content delivery quality. in this article, we review and discuss existing state of the art machine learning-based approaches on enhancing content delivery performance. Discussions in this article will benefit readers and help identify future research agendas and opportunities.

Original language	English
Article number	9220172
Pages (from-to)	228-234
Number of pages	7
Journal	IEEE Network
Volume	34
Issue number	6
DOIs	https://doi.org/10.1109/MNET.011.2000141
Publication status	Published - 1 Nov 2020

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Lu, Z., Gai, K., Duan, Q., Choo, K. K. R., Li, J., Wu, J., & Xu, Y. (2020). Machine Learning Empowered Content Delivery: Status, Challenges, and Opportunities. IEEE Network, 34(6), 228-234. Article 9220172. https://doi.org/10.1109/MNET.011.2000141

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Machine Learning Empowered Content Delivery: Status, Challenges, and Opportunities

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