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Modelling IPTV Services: An In-Depth Analysis

This paper outlines the need for realistic IPTV traffic modeling, detailing dataset analysis and findings on user behavior characteristics, channel popularity, and workload patterns. Analyzing IPTV services is crucial due to its rapid growth and impact on IP networks.

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Modelling IPTV Services: An In-Depth Analysis

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  1. Modelling IPTV Services Fernando Ramos* fernando.ramos@cl.cam.ac.uk *Together with Jon Crowcroft, Ian White, Richard Gibbens, Fei Song, P. Rodriguez

  2. Outline • Introduction: what IPTV is not, and why do we care • Motivation to model IPTV services • The IPTV traffic model, in some detail (W.I.P.) • Conclusions Fernando Ramos, Cosener's Multi-Service Networks

  3. What IPTV is not X X web TV X p2p TV on-demand video service cable-like TV service live TV IPTV is a cable-like TV service offered on top of an IP network Fernando Ramos, Cosener's Multi-Service Networks

  4. Why do we care with IPTV? • One of the fastest growing television services in the world [1] • 2005: 2 million users • 2007: 14 million users • ...and growing • High bandwidth and strict QoS requirements • Big impact in the IP network [1] Parks Associates. Tv services in Europe: Update and Outlook, 2008 Fernando Ramos, Cosener's Multi-Service Networks

  5. Internet Overview of an IPTV network IP Network TV Head End Customer Premises TV . . . STB DSLAM PC Fernando Ramos, Cosener's Multi-Service Networks

  6. Motivation – Why do we need a realistic IPTV Traffic Model? • Brand new service on top of an IP network • User behaviour very different from other IP-based applications Fernando Ramos, Cosener's Multi-Service Networks

  7. Motivation – Why do we need a realistic IPTV Traffic Model? • To evaluate different delivery systems for IPTV • To evaluate different network architectures for IPTV Fernando Ramos, Cosener's Multi-Service Networks

  8. The dataset • We have analysed real IPTV data from one of the largest IPTV service providers • ~ 6 months worth of data • ~ 250,000 customers • ~ 620 DSLAMs • ~ 150 TV channels • NB: We consider a user is zapping if he switches between 2 TV channels in less than 1 minute. Fernando Ramos, Cosener's Multi-Service Networks

  9. IPTV Traffic Model • Workload characteristics • Zapping blocks containing a random number of switching events (zapping period) • Separated by watching/away periods of random length Zap blocks Inter-zap block periods Fernando Ramos, Cosener's Multi-Service Networks

  10. IPTV Traffic Model WATCHING MODE ZAPPING MODE Fernando Ramos, Cosener's Multi-Service Networks

  11. IPTV Traffic Model - Detailed Findings: Empirical data fits with 2 gamma and 1 exponential (consistent across regions) To do: Check consistency for different channels Check consistency for period of the day Fernando Ramos, Cosener's Multi-Service Networks

  12. IPTV Traffic Model - Detailed Findings: Empirical data fits with gamma distribution (consistent across regions) To do: Check consistency for period of the day Fernando Ramos, Cosener's Multi-Service Networks

  13. IPTV Traffic Model - Detailed Findings: Popularity is a) Zipf-like for top channels, b) decays abruptly for non-popular ones. To do: Add dependency of previous channel. Fernando Ramos, Cosener's Multi-Service Networks

  14. Conclusions • Preliminary results of an IPTV Workload model were presented • Some of the main findings: • Workload characteristics: Burst (zapping) periods separated by watch/way periods • Popularity: a) Zipf-like for top channels, b) decays fast for non-popular ones • Watching period empirical data fits with 2 gamma and 1 exponential distributions • Number of channels in a zap period fits with gamma distribution • See you at the SIGCOMM Poster Session!  Fernando Ramos, Cosener's Multi-Service Networks

  15. THANK YOU! • fernando.ramos@cl.cam.ac.uk Fernando Ramos, Cosener's Multi-Service Networks

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