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

Research output: Contribution to journalArticlepeer-review

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 languageEnglish (US)
Article number9220172
Pages (from-to)228-234
Number of pages7
JournalIEEE Network
Volume34
Issue number6
DOIs
StatePublished - Nov 1 2020

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications

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