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Modeling channel popularity dynamics in a large IPTV system
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Joint International Conference on Measurement and Modeling of Computer Systems archive
Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems table of contents
Seattle, WA, USA
SESSION: Measurement table of contents
Pages 275-286  
Year of Publication: 2009
ISBN:978-1-60558-511-6
Authors
Tongqing Qiu  Georgia Tech, Atlanta, GA, USA
Zihui Ge  AT&T Labs -- Research, Florham Park, NJ, USA
Seungjoon Lee  AT&T Labs - Research, Florham Park, NJ, USA
Jia Wang  AT&T Labs - Research, Florham Park, NJ, USA
Qi Zhao  AT&T Labs - Research, Florham Park, NJ, USA
Jun Xu  Georgia Tech, Atlanta, GA, USA
Sponsors
ACM: Association for Computing Machinery
SIGMETRICS: ACM Special Interest Group on Measurement and Evaluation
Publisher
ACM  New York, NY, USA
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ABSTRACT

Understanding the channel popularity or content popularity is an important step in the workload characterization for modern information distribution systems (e.g., World Wide Web, peer-to-peer file-sharing systems, video-on-demand systems).

In this paper, we focus on analyzing the channel popularity in the context of Internet Protocol Television (IPTV). In particular, we aim at capturing two important aspects of channel popularity - the distribution and temporal dynamics of the channel popularity. We conduct in-depth analysis on channel popularity on a large collection of user channel access data from a nation-wide commercial IPTV network. Based on the findings in our analysis, we choose a stochastic model that finds good matches in all attributes of interest with respect to the channel popularity. Furthermore, we propose a method to identify subsets of user population with inherently different channel interest.

By tracking the change of population mixtures among different user classes, we extend our model to a multi-class population model, which enables us to capture the moderate diurnal popularity patterns exhibited in some channels. We also validate our channel popularity model using real user channel access data from commercial IPTV network.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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Collaborative Colleagues:
Tongqing Qiu: colleagues
Zihui Ge: colleagues
Seungjoon Lee: colleagues
Jia Wang: colleagues
Qi Zhao: colleagues
Jun Xu: colleagues