Journal of Signal Processing Systems

, Volume 91, Issue 10, pp 1149–1157 | Cite as

A Statistic and Analysis of Access Pattern for Online VoD Multimedia

  • Zhijie HanEmail author
  • Ji_ao Ma
  • Xin He
  • Weibei Fan


The generally accepted that Zipf-Distribution is a convinced access pattern for text-based Web. However, with the dramatic increasement of VoD media traffic on the Internet such as Flash P2P, the inconsistency between the access patterns of media objects and the Zipf model has been researched by many scholars. In this paper, we have studied a large variety of media work-loads collected from both browser and server sides in Adobe Flash P2P systems which applied in Youku, Youtube, etc. Through extensive analysis and modeling. And found the object reference ranks of all these workloads follow the logistic (LOG) distribution despite their different media systems and delivery methods by extensive analysis and modeling. This mean it does not follow long tail effect; Furthermore, we have constructed mathematical models which can applied in access pattern in FlashP2P traffic. By analyzing the model of media traffic access, it is possible to better describe the user’s access mode. Meantime, it is very suitable for the configuration and allocation of network resources which can be used more efficiently.


Traffic analysis Modeling Access pattern Logistic distribution 



The subject is sponsored by the National Natual Science Foundation of China (61672209, 61701170) China Postdoctoral Science Foundation funded project (2014 M560439), Jiangsu Planned Projects for Postdoctoral Research Funds (1302084B) Scientific & Technological Support Project of Jiangsu Province (BE2016185).


  1. 1.
    Content Delivery NetwoAs[A]. Lecture Notes Elecuical Engineering, ed. Rajkumar Buyya, Mukaddim Pathan, and AthenaVakall[C]. Springef: Berlin, 2008.Google Scholar
  2. 2.
    Lee Jack, Y. B. (2005). Scalable continuous media strearning systems: Architecture,design,analysis and implementation[M]. New York: Wiley.Google Scholar
  3. 3.
    Tang, W.-L., Fu, Y., Chetkasova, L., et al. (2003). MediSyn: a synthetic streaming media service workload genennor[c]. NOSSDAV'03: Proceedings ofthe 13th inteznafional workshop on Network and operating systems support for digital audio and video, Monterey, CA, USA. New York, NY, USA: ACM.Google Scholar
  4. 4.
    Acharya, S., Smith, B., Pames, P. (1999). Charactedzingus user access to videos on the world wide web[c]. Proceedings of SPIE.Google Scholar
  5. 5.
    Yu, H.-L., Zheng, D.-D., Zhao Ben, Y., et al. (2006). Understanding 1lscr behavior in large-scale video-on-demand systems[J]. SIGOPS Operating Systems Review, 40(4), 333–344.CrossRefGoogle Scholar
  6. 6.
    Veloso, E., Almeida, V., Meira, W., Bestavros, A., Jin, S. (2002). A hierarchical characterization of a live streaming media workload. In Proc. of ACM SIGCOMM IMW.Google Scholar
  7. 7.
    Sripanidkulchai, K., Maggs, B., Zhang, H. (2004). Ananalysis of live streaming workloads on the internet. In Proc. of ACM SIGCOMM IMC.Google Scholar
  8. 8.
    Gummadi, K. P., Dunn, R. J., Saroiu, S., Gribble, S. D., Levy, H. M., Zahorjan, J. (2003). Measurement, modeling, and analysis of a peer-to-peer file-sharing workload. InProc. of ACM SOSP.Google Scholar
  9. 9.
    Iamnitchi, A., Ripeanu, M., Foster, I. (2004). Small-world file-sharing communities. In Proc. of IEEE INFOCOM.Google Scholar
  10. 10.
    Gill, P., Arlitt, M., Li, Z., Mahanti, A. (2007). YouTube traffic characterization: A view from the edge. In Proc.of ACM SIGCOMM IMC.Google Scholar
  11. 11.
    Cha, M., Kwak, H., Rodriguez, P., Ahn, Y., Moon, S. (2007). I tube, you tube, everybody tubes: analyzing the world’s largest user generated content video system. In Proc. of ACM SIGCOMM IMC.Google Scholar
  12. 12.
    Tang, W., Fu, Y., Cherkasova, L., Vahdat, A. (2003). MediSyn: A synthetic streaming media service workload generator. In Proc. of ACM NOSSDAV.Google Scholar
  13. 13.
    Bonney, G. E. (1986). Regressive logistic models for familial disease and other binary traits. Biometrics, 42(3), 61.CrossRefGoogle Scholar
  14. 14.
    Pearce, J., & Ferrier, S. (2000). Evaluating the predictive performance of habitat models developed using logistic regression. Ecological Modelling, 133(3), 225–245.CrossRefGoogle Scholar
  15. 15.
    Steyerberg, E. W., et al. (2001). Internal validation of predictive models: Efficiency of some procedures for logistic regression analysis. Journal of Clinical Epidemiology, 54(8), 774.CrossRefGoogle Scholar
  16. 16.
    Hosmer, D. W., et al. (1997). A comparison of goodness-of-fit tests for the logistic regression model. Statistics in Medicine, 16(9), 965–980.CrossRefGoogle Scholar
  17. 17.
    Prentice, R. L., & Pyke, R. (1979). Logistic disease incidence models and case-control studies. Biometrika, 66(3), 403–411.MathSciNetCrossRefGoogle Scholar
  18. 18.
    Fan, W., Han, Z., & Wang, R. (2018). An evaluation model and benchmark for parallel computing frameworks. Mobile Information Systems, 3890341, 1–14.Google Scholar
  19. 19.
    Fan, W., Han, Z., Li, P., Zhou, J., Fan, J., Wang, R. (2018). Journal of Signal Processing Systems, 1–13. Scholar
  20. 20.
    Li, Y., et al. (2016). Loop parallelism maximization for multimedia DSP in mobile vehicular clouds. IEEE Transactions on Cloud Computing, 99, 1.Google Scholar
  21. 21.
    Li, Y., et al. (2016). Privacy protection for preventing data over-collection in Smart City. IEEE Transactions on Computers, 65(5), 1339–1350.MathSciNetCrossRefGoogle Scholar
  22. 22.
    Zhu, X., Qin, X., & Qiu, M. (2011). QoS-aware fault-tolerant scheduling for real-time tasks on heterogeneous clusters. IEEE Transactions on Computers, 60(6), 800–812.MathSciNetCrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Data and Knowledge EngineeringHenan UniversityKaifengChina
  2. 2.Jiangsu High Technology Research Key Laboratory for Wireless Sensor NetworkNanjingChina
  3. 3.School of Software Henan UniversityKaifengChina
  4. 4.School of Computer Science and TechnologySoochow UniversitySuzhouChina

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