ZTE Communications ›› 2018, Vol. 16 ›› Issue (3): 30-39.DOI: 10.19729/j.cnki.1673-5188.2018.03.006

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When Machine Learning Meets Media Cloud: Architecture, Application and Outlook

JIN Yichao, WEN Yonggang   

  1. School of Computer Science and Engineering, Nanyang Technological University, 639798, Singapore
  • Online:2018-08-25 Published:2020-03-18
  • About author:JIN Yichao (yjin3@ntu.edu.sg) received the B.S and M.S degree from Nanjing University of Posts and Telecommunications (NUPT), China, in 2008 and 2011 respectively, and Ph.D degree from School of Computer Science and Engineering, Nanyang Technological University (NTU), Singapore, in 2016. His research interests are cloud computing and multimedia network.|WEN Yonggang (ygwen@ntu.edu.sg) is an associate professor with School of Computer Science and Engineering at Nanyang Technological University, Singapore. He received his PhD degree in Electrical Engineering and Computer Science (minor in Western Literature) from Massachusetts Institute of Technology (MIT), Cambridge, USA. Previously he has worked in Cisco to lead product development in content delivery network, which had a revenue impact of 3 Billion US dollars globally. Dr. Wen has published over 150 papers in top journals and prestigious conferences. His research interests include cloud computing, green data center, big data analytics, multimedia network and mobile computing.


Nowadays, media cloud and machine learning have become two hot research domains. On the one hand, the increasing user demand on multimedia services has triggered the emergence of media cloud, which uses cloud computing to better host media services. On the other hand, machine learning techniques have been successfully applied in a variety of multimedia applications as well as a list of infrastructure and platform services. In this article, we present a tutorial survey on the way of using machine learning techniques to address the emerging challenges in the infrastructure and platform layer of media cloud. Specifically, we begin with a review on the basic concepts of various machine learning techniques. Then, we examine the system architecture of media cloud, focusing on the functionalities in the infrastructure and platform layer. For each of these function and its corresponding challenge, we further illustrate the adoptable machine learning based approaches. Finally, we present an outlook on the open issues in this intersectional domain. The objective of this article is to provide a quick reference to inspire the researchers from either machine learning or media cloud area.

Key words: machine learning, media cloud