Modeling the Content Popularity Evolution in Video-on-Demand Systems

  • Attila Kőrösi
  • Balázs Székely
  • Miklós Máté
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 63)


The simulation and testing of Video-on-Demand (VoD) services require the generation of realistic content request patterns to emulate a virtual user base. The efficiency of these services depend on the popularity distribution of the video library, thus the traffic generators have to mimic the statistical properties of real life video requests. In this paper the connection among the content popularity descriptors of a generic VoD service is investigated. We provide an analytical model for the relationships among the most important popularity descriptors, such as the ordered long term popularity of the whole video library, the popularity evolutions and the initial popularity of the individual contents. Beyond the theoretical interest, our method provides a simple way of generating realistic request patterns for simulating or testing media servers.


Video popularity analytical model 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Breslau, L., Cao, P., Fan, L., Phillips, G., Shenker, S.: Web caching and zipf-like distributions: evidence and implications. In: Proceedings of Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies, vol. 1, pp. 126–134. IEEE, Los Alamitos (1999)Google Scholar
  2. 2.
    Dantzig, G.B., Thapa, M.N.: Linear programming 1: Introduction. Springer, Heidelberg (1997)zbMATHGoogle Scholar
  3. 3.
    Guo, L., Tan, E., Chen, S., Xiao, Z., Zhang, X.: The stretched exponential distribution of internet media access patterns. In: Proc. of PODC 2008, Toronto, Canada (August 2008)Google Scholar
  4. 4.
    Mandelbrot, B.: Information Theory and Psycholinguistics. Penguin Books (1968)Google Scholar
  5. 5.
    Pallis, G., Vakali, A.: Insight and perspectives for content delivery networks. Communications of the ACM, 101–106 (January 2006)Google Scholar
  6. 6.
    Tang, W., Fu, Y., Cherkasova, L., Vahdat, A.: Modeling and generating realistic streaming media server workloads. Comput. Netw. 51(1), 336–356 (2007)CrossRefzbMATHGoogle Scholar
  7. 7.
    Yu, H., Zheng, D., Zhao, B.Y., Zheng, W.: Understanding user behavior in large-scale video-on-demand systems. In: Proc. of Eurosys 2006, Leuven, Belgium, pp. 333–344 (2006)Google Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2011

Authors and Affiliations

  • Attila Kőrösi
    • 1
  • Balázs Székely
    • 2
  • Miklós Máté
    • 1
  1. 1.Department of Telecommunication and Media InformaticsBudapest University of Technology and EconomicsHungary
  2. 2.Institute of MathematicsBudapest University of Technology and EconomicsHungary

Personalised recommendations