Multimedia Systems

, Volume 17, Issue 1, pp 5–18 | Cite as

Performance analysis of a caching algorithm for a catch-up television service

  • Z. Avramova
  • D. De Vleeschauwer
  • S. Wittevrongel
  • H. Bruneel
Regular Paper

Abstract

The catch-up TV (CUTV) service allows users to watch video content that was previously broadcast live on TV channels and later placed on an on-line video store. Upon a request from a user to watch a recently missed episode of his/her favourite TV series, the content is streamed from the video server to the customer’s receiver device. This requires that an individual flow is set up for the duration of the video, and since it is hard to impossible to employ multicast streaming for this purpose (as users seldomly issue a request for the same episode at the same time), these flows are unicast. In this paper, we demonstrate that with the growing popularity of the CUTV service, the number of simultaneously running unicast flows on the aggregation parts of the network threaten to lead to an unwieldy increase in required bandwidth. Anticipating this problem and trying to alleviate it, the network operators deploy caches in strategic places in the network. We investigate the performance of such a caching strategy and the impact of its size and the cache update logic. We first analyse and model the evolution of video popularity over time based on traces we collected during 10 months. Through simulations we compare the performance of the traditional least-recently used and least-frequently used caching algorithms to our own algorithm. We also compare their performance with a “perfect” caching algorithm, which knows and hence does not have to estimate the video request rates. In the experimental data, we see that the video parameters from the popularity evolution law can be clustered. Therefore, we investigate theoretical models that can capture these clusters and we study the impact of clustering on the caching performance. Finally, some considerations on the optimal cache placement are presented.

Keywords

IPTV On-demand services Caching Catch-up TV 

References

  1. 1.
    ATIS Standard. IPTV high level architecture (ATIS-0800007) (2007)Google Scholar
  2. 2.
    Avramova, Z., De Vleeschauwer, D., Wittevrongel, S., Bruneel, H.: Analysis and modeling of video popularity evolution in various online video content systems: power-law versus exponential decay. In: Proceedings of the first international conference on evolving Internet INTERNET 2009 (August 2009, Cannes, France), pp. 95–100 (2009)Google Scholar
  3. 3.
    Avramova, Z., De Vleeschauwer, D., Wittevrongel, S., Bruneel, H.: Capacity gain of mixed multicast/unicast transport schemes in a TV distribution network. IEEE Trans. Multimed. 11, 918–931 (2009)CrossRefGoogle Scholar
  4. 4.
    Baggio, A., Van Steen, M.: Distributed redirection for the world-wide web. Comput. Netw. 49(6), 743–765 (2005)CrossRefGoogle Scholar
  5. 5.
    Breslau, L., Cao, P., Fan, L., Phillips, G., Shenker, S.: Web caching and Zipf-like distributions: evidence and implications. In: Proceedings of the eighteenth annual joint conference of the IEEE computer and communications societies (INFOCOM99), vol. 1 (New York (NY), USA, March 21–25, 1999), pp. 126–134 (1999)Google Scholar
  6. 6.
    Cárdenas, L.G., Gil, J.A., Domènech, J., Sahuquillo, J., Pont, A.: Performance comparison of a Web cache simulation framework. In: Proceedings of the 19th international conference on advanced information networking and applications (AINA05), vol. 2 (Taipei, Taiwan, March 28–30 2005), pp. 281–284 (2005)Google Scholar
  7. 7.
    Chien, W.-D., Yeh, Y.-S., Wang, J.-S.: Practical channel transition for near-VOD services. IEEE Trans. Broadcast. 51(3), 360–365 (2005)CrossRefGoogle Scholar
  8. 8.
    De Vleeschauwer, D., Laevens, K.: Performance of caching algorithms for IPTV on-demand services. IEEE Trans. Broadcast. 55(2009), 491–501 (2009)CrossRefGoogle Scholar
  9. 9.
    Ho, K.-M., Poon, W.-F., Lo, K.-T.: Performance study of large-scale video streaming services in highly heterogeneous environment. IEEE Trans. Broadcast. 53(4), 763–773 (2007)CrossRefGoogle Scholar
  10. 10.
    Krogfoss, B., Sofman, L., Agrawal, A.: Caching architectures and optimization strategies for IPTV Networks. Bell Labs Tech. J. 13(3), 13–28 (2008)CrossRefGoogle Scholar
  11. 11.
    Nikolaus, B., Ott, J., Bormann, C., Bormann, U.: Generalized greedy broadcasting for efficient media-on-demand transmissions. IEEE Trans. Broadcast. 51(3), 354–359 (2005)CrossRefGoogle Scholar
  12. 12.
    Poon, W.-F., Lo, K.-T., Feng, J.: Provision of continuous VCR functions in interactive broadcast VoD systems. IEEE Trans. Broadcast. 51(4), 460–472 (2005)CrossRefGoogle Scholar
  13. 13.
    Rabinovich, M., Spatscheck, O.: Web caching and replication. Addison-Wesley, USA (2002)Google Scholar
  14. 14.
    Shi, L., Gu, Z., Wei, L., Shi, Y.: An applicative study of Zipf’s law on Web Cache. Int. J. Inf. Technol. 12, 49–58 (2006)Google Scholar
  15. 15.
    Verhoeyen, M., De Vleeschauwer, D., Robinson, D.: Content storage architectures for boosted IPTV service. Bell Labs Tech. J. 13(3), 29–43 (2008)CrossRefGoogle Scholar
  16. 16.
    Wauters, T., et al.: HFC access network design for switched broadcast TV services. IEEE Trans. Broadcast. 53, 588–594 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Z. Avramova
    • 1
  • D. De Vleeschauwer
    • 2
  • S. Wittevrongel
    • 1
  • H. Bruneel
    • 1
  1. 1.SMACS Research Group, TELIN, Faculty of EngineeringGhent UniversityGhentBelgium
  2. 2.Bell Labs, Alcatel-Lucent BellAntwerpBelgium

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