research-article

Modeling channel popularity dynamics in a large IPTV system

Authors:

Jun XuAuthors Info & Claims

SIGMETRICS '09: Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems

Pages 275 - 286

https://doi.org/10.1145/1555349.1555381

Published: 15 June 2009 Publication History

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

[1]

P. Barford and M. Crovella. Generating representative web workloads for network and server performance evaluation. In SIGMETRICS, pages 151--160, 1998.

Digital Library

[2]

J. Bradley. Distribution-free statistical tests. Prentice-Hall., 1968.

[3]

M. Cha, H. Kwak, P. Rodriguez, Y.-Y. Ahn, and S. Moon. I Tube, You Tube, Everybody Tubes: Analyzing the World's Largest User Generated Content Video System. In Proceedings of ACM IMC, 2007.

Digital Library

[4]

M. Cha, P. Rodriguez, J. Crowcroft, S. Moon, and X. Amatrianin. Watching Television Over an IP Network. In Proceedings of ACM IMC, 2008.

Digital Library

[5]

L. Cherkasova and M. Gupta. Characterizing locality, evolution, and life span of accesses in enterprise media server workloads. In NOSSDAV, 2002.

Digital Library

[6]

M. Chesire, A. Wolman, G. M. Voelker, and H. M. Levy. Measurement and analysis of a streaming media workload. In USITS, pages 1--12, 2001.

Digital Library

[7]

J. Chu, K. Labonte, and B. Levine. Availability and locality measurements of peer-to-peer file systems. In Proceedings of ITCom: Scalability and Traffic Control in IP Networks, 2002.

[8]

C. P. Costa, I. S. Cunha, A. B. Vieira, C. V. Ramos, M. M. Rocha, J. M. Almeida, and B. A. Ribeiro-Neto. Analyzing client interactivity in streaming media. In WWW, 2004.

Digital Library

[9]

J. L. Doob. The Brownian movement and stochastic equations. Annals of Math, 40(1):351--369, 1942.

[10]

L. Guo, E. Tan, S. Chen, Z. Xiao, and X. Zhang. The stretched exponential distribution of Internet media access patterns. In PODC, pages 283--294, 2008.

Digital Library

[11]

X. Hei, C. Liang, J. Liang, Y. Liu, and K. W. Ross. A measurement study of a large-scale P2P IPTV system. IEEE Transactions on Multimedia, 9(8):1672--1687, 2007.

Digital Library

[12]

Y. Huang, T. Z. J. Fu, D.-M. Chiu, J. C. S. Lui, and C. Huang. Challenges, Design and Analysis of a Large-scale P2P-VoD System. In Proc. ACM SIGCOMM, 2008.

Digital Library

[13]

J. B. MacQueen. Some methods for classification and analysis of multivariate observations,. In Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, pages 281--297, 1967.

[14]

J. Nielsen. Zipf curves and website popularity, www.useit.com/alertbox/zipf.html, 1997.

[15]

T. Silverston, O. Fourmaux, K. Salamatian, and K. Cho. Measuring P2P IPTV traffic on both sides of the world. In CoNEXT, page 39, 2007.

Digital Library

[16]

D. E. Smith. IPTV Bandwidth Demand: Multicast and Channel Surfing. In INFOCOM, pages 2546--2550, 2007.

[17]

W. Tang, Y. Fu, L. Cherkasova, and A. Vahdat. Medisyn: a synthetic streaming media service workload generator. In NOSSDAV '03, pages 12--21, 2003.

Digital Library

[18]

G. Uhlenbeck and L. Ornstein. On the Theory of Brownian Motion. Physical Review, September 1930.

[19]

Y. Yang. Expert network: effective and efficient learning from human decisions in text categorization and retrieval. In SIGIR 94, pages 13--22, New York, NY, USA, 1994. Springer-Verlag New York, Inc.

Digital Library

[20]

H. Yu, D. Zheng, B. Y. Zhao, and W. Zheng. Understanding user behavior in large-scale video-on-demand systems. SIGOPS Oper. Syst. Rev., 40(4):333--344, 2006.

Digital Library

[21]

H. Yu, D. Zheng, B. Y. Zhao, and W. Zheng. Understanding user behavior in large-scale video-on-demand systems. In EuroSys, pages 333--344, 2006.

Digital Library

Cited By

Malcata TSebastião NDias TRoma N(2023)Neural Network Predictor for Fast Channel Change on DVB Set-Top-BoxesDesign and Architecture for Signal and Image Processing10.1007/978-3-031-29970-4_4(40-52)Online publication date: 27-Apr-2023
https://doi.org/10.1007/978-3-031-29970-4_4
Yan HLin TZeng MWu JLi YJin D(2021)Discovering Usage Patterns of Mobile Video Service in the Cellular NetworksIEEE Transactions on Network and Service Management10.1109/TNSM.2020.304348218:2(1789-1802)Online publication date: Jun-2021
https://doi.org/10.1109/TNSM.2020.3043482
Li GQiu LYu CCao HLiu YYang C(2021)IPTV Channel Zapping Recommendation With Attention MechanismIEEE Transactions on Multimedia10.1109/TMM.2020.298409423(538-549)Online publication date: 2021
https://doi.org/10.1109/TMM.2020.2984094
Show More Cited By

Index Terms

Modeling channel popularity dynamics in a large IPTV system
1. General and reference
  1. Cross-computing tools and techniques
    1. Measurement
    2. Metrics
2. Networks
  1. Network services

Recommendations

Modeling user activities in a large IPTV system
IMC '09: Proceedings of the 9th ACM SIGCOMM conference on Internet measurement

Internet Protocol Television (IPTV) has emerged as a new delivery method for TV. In contrast with native broadcast in traditional cable and satellite TV system, video streams in IPTV are encoded in IP packets and distributed using IP unicast and ...
Modeling channel popularity dynamics in a large IPTV system
SIGMETRICS '09

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,...
Traffic Analysis of Popular Peer-to-Peer IPTV VoD Systems
ICEICE '12: Proceedings of the 2012 Second International Conference on Electric Information and Control Engineering - Volume 03

In order to gain insights to the traffic characteristics and peer behavior characteristics of the VoD systems of the four most popular P2P IPTV, namely PPTV, PP Stream, Kankan and QQ live, this paper develops and deploys a passive network measurement ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

SIGMETRICS '09: Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems

June 2009

336 pages

ISBN:9781605585116

DOI:10.1145/1555349

General Chairs:
John Douceur
Microsoft Research, USA
,
Albert Greenberg
Microsoft Research, USA
,
Program Chairs:
Thomas Bonald
Orange Labs, France
,
Jason Nieh
Columbia University, USA

ACM SIGMETRICS Performance Evaluation Review Volume 37, Issue 1
SIGMETRICS '09
June 2009
320 pages
ISSN:0163-5999
DOI:10.1145/2492101
Issue’s Table of Contents

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 June 2009

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SIGMETRICS09

Sponsor:

SIGMETRICS09: ACM SIGMETRICS/PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems

June 15 - 19, 2009

WA, Seattle, USA

Acceptance Rates

Overall Acceptance Rate 459 of 2,691 submissions, 17%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

111
Total Citations
View Citations
934
Total Downloads

Downloads (Last 12 months)11
Downloads (Last 6 weeks)1

Reflects downloads up to 20 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Malcata TSebastião NDias TRoma N(2023)Neural Network Predictor for Fast Channel Change on DVB Set-Top-BoxesDesign and Architecture for Signal and Image Processing10.1007/978-3-031-29970-4_4(40-52)Online publication date: 27-Apr-2023
https://doi.org/10.1007/978-3-031-29970-4_4
Yan HLin TZeng MWu JLi YJin D(2021)Discovering Usage Patterns of Mobile Video Service in the Cellular NetworksIEEE Transactions on Network and Service Management10.1109/TNSM.2020.304348218:2(1789-1802)Online publication date: Jun-2021
https://doi.org/10.1109/TNSM.2020.3043482
Li GQiu LYu CCao HLiu YYang C(2021)IPTV Channel Zapping Recommendation With Attention MechanismIEEE Transactions on Multimedia10.1109/TMM.2020.298409423(538-549)Online publication date: 2021
https://doi.org/10.1109/TMM.2020.2984094
Li LWang MXue KCheng QWang DChen WPan MHan Z(2020)Delay Optimization in Multi-UAV Edge Caching Networks: A Robust Mean Field GameIEEE Transactions on Vehicular Technology10.1109/TVT.2020.3045509(1-1)Online publication date: 2020
https://doi.org/10.1109/TVT.2020.3045509
Kim HPark JBennis MKim SDebbah M(2020)Mean-Field Game Theoretic Edge Caching in Ultra-Dense NetworksIEEE Transactions on Vehicular Technology10.1109/TVT.2019.295313269:1(935-947)Online publication date: Jan-2020
https://doi.org/10.1109/TVT.2019.2953132
Huang CGan ZYe FWang PZhang M(2020)KNCR: Knowledge-Aware Neural Collaborative Ranking for Recommender Systems2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00066(339-344)Online publication date: Aug-2020
https://doi.org/10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00066
Ntougias KPapadias CPapageorgiou GHasslinger GSorensen T(2020)Coordinated Caching and QoS-Aware Resource Allocation for Spectrum SharingWireless Personal Communications10.1007/s11277-020-07236-y126:1(49-79)Online publication date: 26-Mar-2020
https://doi.org/10.1007/s11277-020-07236-y
Ntougias KPapadias CPapageorgiou GHasslinger G(2019)Spectral Coexistence of 5G Networks and Satellite Communication Systems Enabled by Coordinated Caching and QoS-Aware Resource Allocation2019 27th European Signal Processing Conference (EUSIPCO)10.23919/EUSIPCO.2019.8903063(1-5)Online publication date: Sep-2019
https://doi.org/10.23919/EUSIPCO.2019.8903063
Singhal CChiasserini CCasetti C(2019)EMB: Efficient Multimedia Broadcast in Multi-Tier Mobile NetworksIEEE Transactions on Vehicular Technology10.1109/TVT.2019.293840668:11(11186-11199)Online publication date: Nov-2019
https://doi.org/10.1109/TVT.2019.2938406
Zhao ZVuran MBatur DEkici E(2019)Shades of White: Impacts of Population Dynamics and TV Viewership on Available TV SpectrumIEEE Transactions on Vehicular Technology10.1109/TVT.2019.289286768:3(2427-2442)Online publication date: Mar-2019
https://doi.org/10.1109/TVT.2019.2892867
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents