A Longitudinal Characterization of Local and Global BitTorrent Workload Dynamics

  • Niklas Carlsson
  • György Dán
  • Anirban Mahanti
  • Martin Arlitt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7192)


Workload characterization is important for understanding how systems and services are used in practice and to help identify design improvements. To better understand the longitudinal workload dynamics of chunk-based content delivery systems, this paper analyzes the BitTorrent usage as observed from two different vantage points. Using two simultaneously collected 48-week long traces, we analyze the differences in download characteristics and popularity dynamics observed locally at a university campus versus at a global scale. We find that campus users typically download larger files and are early adopters of new content, in the sense that they typically download files well before the time at which the global popularity of the files peak. The noticeable exception is music files, which the campus users are late to download. We also find that there typically is high churn in the set of files that are popular each week, both locally and globally, and that the most popular files peak significantly later than their release date. These findings provide insights that may improve the efficiency of content sharing locally, and thus increase the scalability of the global system.


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  1. 1.
    Barabasi, A., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)MathSciNetCrossRefMATHGoogle Scholar
  2. 2.
    Bharambe, A.R., Herley, C., Padmanabhan, V.N.: Analyzing and Improving a BitTorrent Network’s Performance Mechanisms. In: Proc. IEEE INFOCOM (April 2006)Google Scholar
  3. 3.
    Borghol, Y., Mitra, S., Ardon, S., Carlsson, N., Eager, D., Mahanti, A.: Characterizing and modeling popularity of user-generated videos. In: Proc. IFIP PERFORMANCE, Amsterdam, Netherlands (October 2011)Google Scholar
  4. 4.
    Breslau, L., Cao, P., Fan, L., Phillips, G., Shenker, S.: Web Caching and Zipf-like Distributions: Evidence and Implications. In: Proc. IEEE INFOCOM (March 1999)Google Scholar
  5. 5.
    Cha, M., Kwak, H., Rodriguez, P., Ahn, Y., Moon, S.: I Tube, You Tube, Everybody Tubes: Analyzing the World’s Largest User Generated Content Video System. In: Proc. ACM IMC (2007)Google Scholar
  6. 6.
    Cheng, X., Dale, C., Lui, J.: Understanding the characteristics of internet short video sharing: Youtube as a case study. In: Proc. IWQoS (2008)Google Scholar
  7. 7.
    Dán, G., Carlsson, N.: Power-law revisited: A large scale measurement study of P2P content popularity. In: Proc. International Workshop on Peer-to-Peer Systems (IPTPS) (April 2010)Google Scholar
  8. 8.
    Gill, P., Arlitt, M., Li, Z., Mahanti, A.: YouTube Traffic Characterization: A View from the Edge. In: Proc. ACM IMC (2007)Google Scholar
  9. 9.
    Gummadi, K., Dunn, R., Saroiu, S., Gribble, S., Levy, H., Zahorjan, J.: Measurement, modeling, and analysis of a peer-to-peer file-sharing workload. In: Proc. SOSP (2003)Google Scholar
  10. 10.
    Guo, L., Chen, S., Xiao, Z., Tan, E., Ding, X., Zhang, X.: Measurement, Analysis, and Modeling of BitTorrent-like Systems. In: Proc. ACM IMC (October 2005)Google Scholar
  11. 11.
    Hefeeda, M., Saleh, O.: Traffic modeling and proportional partial caching for peer-to-peer systems. IEEE/ACM Trans. on Networking 16(6), 1447–1460 (2008)CrossRefGoogle Scholar
  12. 12.
    Klemm, A., Lindemann, C., Vernon, M.K., Waldhorst, O.P.: Characterizing the query behavior in peer-to-peer file sharing systems. In: Proc. ACM IMC (2004)Google Scholar
  13. 13.
    Legout, A., Urvoy-Keller, G., Michiardi, P.: Rarest First and Choke Algorithms Are Enough. In: Proc. ACM IMC (October 2006)Google Scholar
  14. 14.
    Menasche, D., Rocha, A., Li, B., Towsley, D., Venkataramani, A.: Content Availability in Swarming Systems: Models, Measurements and Bundling Implications. In: ACM CoNEXT (December 2009)Google Scholar
  15. 15.
    Mitra, S., Agrawal, M., Yadav, A., Carlsson, N., Eager, D., Mahanti, A.: Characterizing web-based video sharing workloads. ACM Tran. on the Web (2), 8:1–8:27 (2011)Google Scholar
  16. 16.
    Pouwelse, J.A., Garbacki, P., Epema, D.H.J., Sips, H.J.: The Bittorrent P2P File-Sharing System: Measurements and Analysis. In: van Renesse, R. (ed.) IPTPS 2005. LNCS, vol. 3640, pp. 205–216. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  17. 17.
    Wierzbicki, A., Leibowitz, N., Ripeanu, M., Woźniak, R.: Cache replacement policies for P2P file sharing protocols. Euro. Trans. on Telecomms. 15, 559–569 (2004)CrossRefGoogle Scholar
  18. 18.
    Yu, H., Zheng, D., Zhao, B., Zheng, W.: Understanding User Behavior in Large-Scale Video-on-Demand Systems. SIGOPS Oper. Syst. Rev. 40(4), 333–344 (2006)CrossRefGoogle Scholar
  19. 19.
    Zhang, B., Iosup, A., Pouwelse, J.A., Epema, D.: Identifying, analyzing, and modeling flashcrowds in bittorrent. In: Proc. IEEE Peer-to-Peer Computing, Kyoto, Japan (August/September 2011) Google Scholar
  20. 20.
    Zhang, C., Dhungel, P., Wu, D., Ross, K.W.: Unraveling the bittorrent ecosystem. IEEE Transactions on Parallel and Distributed Systems 22, 1164–1177 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Niklas Carlsson
    • 1
  • György Dán
    • 2
  • Anirban Mahanti
    • 3
  • Martin Arlitt
    • 4
    • 5
  1. 1.Linköping UniversitySweden
  2. 2.KTH Royal Institute of TechnologySweden
  3. 3.NICTAAustralia
  4. 4.HP LabsUSA
  5. 5.University of CalgaryCanada

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