DocumentCode
14604
Title
CPCDN: Content Delivery Powered by Context and User Intelligence
Author
Zhi Wang ; Wenwu Zhu ; Minghua Chen ; Lifeng Sun ; Shiqiang Yang
Author_Institution
Grad. Sch. at Shenzhen, Tsinghua Univ., Shenzhen, China
Volume
17
Issue
1
fYear
2015
fDate
Jan. 2015
Firstpage
92
Lastpage
103
Abstract
There is an unprecedented trend that content providers (CPs) are building their own content delivery networks (CDNs) to provide a variety of content services to their users. By exploiting powerful CP-level information in content distribution, these CP-built CDNs open up a whole new design space and are changing the content delivery landscape. In this paper, we adopt a measurement-based approach to understanding why, how, and how much CP-level intelligences can help content delivery. We first present a measurement study of the CDN built by Tencent, a largest content provider based in China. We observe new characteristics and trends in content delivery which pose great challenges to the conventional content delivery paradigm and motivate the proposal of CPCDN, a CDN powered by CP-aware information. We then reveal the benefits obtained by exploiting two indispensable CP-level intelligences, namely context intelligence and user intelligence, in content delivery. Inspired by the insights learnt from the measurement studies, we systematically explore the design space of CPCDN and present the novel architecture and algorithms to address the new content delivery challenges that have arisen. Our results not only demonstrate the potential of CPCDN in pushing content delivery performance to the next level, but also identify new research problems calling for further investigation.
Keywords
content management; data mining; multimedia computing; CP-level information; CPCDN; content delivery networks; content distribution; context intelligence; data mining; multimedia content providers; user intelligence; Atmospheric measurements; Context; Market research; Particle measurements; Social network services; Space exploration; Streaming media; Content delivery; QoS; data mining; user behavior;
fLanguage
English
Journal_Title
Multimedia, IEEE Transactions on
Publisher
ieee
ISSN
1520-9210
Type
jour
DOI
10.1109/TMM.2014.2365364
Filename
6937177
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