Modeling user activities in a large IPTV system

  • Authors:

  • Tongqing Qiu;Zihui Ge;Seungjoon Lee;Jia Wang;Jun Xu;Qi Zhao

  • Affiliations:

  • Georgia Tech, Atlanta, GA, USA;AT&T Labs - Research, Florham Park, NJ, USA;AT&T Labs - Research, Florham Park, NJ, USA;AT&T Labs - Research, Florham Park, NJ, USA;Georgia Tech, Atlanta, GA, USA;AT&T Labs - Research, Florham Park, NJ, USA

  • Venue:
  • Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
  • Year:
  • 2009

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Abstract

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 multicast. This new architecture has been strategically embraced by ISPs across the globe, recognizing the opportunity for new services and its potential toward a more interactive style of TV watching experience in the future. Since user activities such as channel switches in IPTV impose workload beyond local TV or set-top box (different from broadcast TV systems), it becomes essential to characterize and model the aggregate user activities in an IPTV network to support various system design and performance evaluation functions such as network capacity planning. In this work, we perform an in-depth study on several intrinsic characteristics of IPTV user activities by analyzing the real data collected from an operational nation-wide IPTV system. We further generalize the findings and develop a series of models for capturing both the probability distribution and time-dynamics of user activities. We then combine theses models to design an IPTV user activity workload generation tool called SIMUL WATCH, which takes a small number of input parameters and generates synthetic workload traces that mimic a set of real users watching IPTV. We validate all the models and the prototype of SIMUL WATCH using the real traces. In particular, we show that SIMUL WATCH can estimate the unicast and multicast traffic accurately, proving itself as a useful tool in driving the performance study in IPTV systems.