Characterizing Traffic Flows Originating from Large-Scale Video Sharing Services

  • Tatsuya Mori
  • Ryoichi Kawahara
  • Haruhisa Hasegawa
  • Shinsuke Shimogawa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6003)


This work attempts to characterize network traffic flows originating from large-scale video sharing services such as YouTube. The key technical contributions of this paper are twofold. We first present a simple and effective methodology that identifies traffic flows originating from video hosting servers. The key idea behind our approach is to leverage the addressing/naming conventions used in large-scale server farms. Next, using the identified video flows, we investigate the characteristics of network traffic flows of video sharing services from a network service provider view. Our study reveals the intrinsic characteristics of the flow size distributions of video sharing services. The origin of the intrinsic characteristics is rooted on the differentiated service provided for free and premium membership of the video sharing services. We also investigate temporal characteristics of video traffic flows.


Flow Originate Network Service Provider Video Flow Deep Packet Inspection Free Membership 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Abhari, A., Soraya, M.: Workload Generation for YouTube. Multimedia Tools and Applications journal (June 2009)Google Scholar
  2. 2.
    Cha, M., Kwak, H., Rodriguez, P., Ahn, Y.Y., Moon, S.: I Tube, You Tube, Everybody Tubes: Analyzing the World’s Largest User Generated Content Video System. In: Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, pp. 1–14 (2007)Google Scholar
  3. 3.
    Cheng, X., Dale, C., Liu, J.: Understanding the Characteristics of Internet Short Video Sharing: YouTube as a Case Study. CoRR, abs/0707.3670 (2007)Google Scholar
  4. 4.
    Cheng, X., Dale, C., Liu, J.: Statistics and Social Network of YouTube Videos. In: IWQoS 2008, pp. 229–238 (2008)Google Scholar
  5. 5.
    Cisco Systems, Inc. Cisco Visual Networking Index – Forecast and Methodology (2007–2012), (June 2008)
  6. 6.
  7. 7.
    Gill, P., Arlitt, M., Li, Z., Mahanti, A.: Characterizing User Sessions on YouTube. In: Fifteenth Annual Multimedia Computing and Networking Conference, MMCN (2008)Google Scholar
  8. 8.
    Huang, C., Li, J., Ross, K.W.: Can Internet Video-on-Demand Be Profitable? In: ACM SIGCOMM 2007, pp. 133–144 (August 2007)Google Scholar
  9. 9.
    Huang, C., Wang, A., Li, J., Ross, K.W.: Measuring and Evaluating Large-scale CDNs. In: Microsoft Research Technical Report MSR-TR-2008-106 (2008)Google Scholar
  10. 10.
    IRCache project,
  11. 11.
    Kang, X., Zhang, H., Jiang, G., Chen, H., Meng, X., Yoshihira, K.: Measurement, Modeling, and Analysis of Internet Video Sharing Site Workload: A Case Study. In: Proceedings of IEEE International Conference on Web Services, pp. 278–285 (2008)Google Scholar
  12. 12.
  13. 13.
    Mori, T., Takine, T., Pan, J., Kawahara, R., Uchida, M., Goto, S.: Identifying Heavy-Hitter Flows from Sampled Flow Statistics. IEICE Transactions 90-B(11), 3061–3072 (2007)CrossRefGoogle Scholar
  14. 14.
  15. 15.
    Plissonneau, L., En-Najjary, T., Urvoy-Keller, G.: Revisiting Web Traffic from a DSL Provider Perspective: the Case of YouTube. In: Proceedings of the 19th ITC Specialist Seminar (October 2008)Google Scholar
  16. 16.
    Smiley Videos,
  17. 17.
    Willinger, W., Taqqu, M.S., Sherman, R., Wilson, D.V.: Self-similarity through high-variability: statistical analysis of ethernet lan traffic at the source level. IEEE/ACM Trans. Netw. 5(1), 71–86 (1997)CrossRefGoogle Scholar
  18. 18.
  19. 19.
  20. 20.
    Zink, M., Suh, K., Gu, Y., Kurose, J.: Characteristics of YouTube Network Traffic at a Campus Network – Measurements, Models, and Implications. Comput. Netw. 53(4), 501–514 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Tatsuya Mori
    • 1
  • Ryoichi Kawahara
    • 1
  • Haruhisa Hasegawa
    • 1
  • Shinsuke Shimogawa
    • 1
  1. 1.NTT Research Laboratories, 3–9–11 MidorichoMusashino-city, TokyoJapan

Personalised recommendations