Journal of Communications and Networks

The Korean Institute of Commucations and Information Sciences (한국통신학회)
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Analysis of Aggregated HTTP-based Video Traffic

  • Received : 2015.05.25
  • Accepted : 2016.02.24
  • Published : 2016.10.31
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Abstract

Increase of hypertext transfer protocol (HTTP)-based video popularity causes that broadband and Internet service providers' links transmit mainly multimedia content. Network planning, traffic engineering or congestion control requires understanding of the statistical properties of network traffic; therefore, it is desirable to investigate the characteristic of traffic traces generated, among others, by systems which employ adaptive bit-rate streaming. In our work, we investigate traffic originating from 120 client-server pairs, situated in an emulated laboratory environment, and multiplexed onto a single network link. We show that the structure of the traffic is distinct from the structure generated by first and second generation of HTTP video systems, and furthermore, not similar to the structure of general Internet traffic. The obtained traffic exhibits negative correlations, anti-persistence, and its distribution function is skewed to the right. Furthermore, we show that the traffic generated by clients employing the same or similar play-out strategies is positively correlated and synchronised (clustered), whereas traffic originated from different play-out strategies shows negative or no correlations.

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References

  1. YouTube statistics. [Online] Available: https://www.youtube.com/yt/press/en/statistics.html.
  2. Cisco technical paper. (2014). Cisco visual networking index: Forecast and methodology, 2013-2018. [Online]. Available: http://www.cisco.com/web/KR/inspire/2014 07/pdf/2014 vnireport kor.pdf.
  3. I. Sodagar, "The MPEG-dash standard for multimedia streaming over the Internet," IEEE MultiMedia, vol. 18, no. 4, pp. 62-67, Apr. 2011. https://doi.org/10.1109/MMUL.2011.71
  4. W. Willinger, M. S. Taqqu, R. Sherman, and D. V. Wilson, "Selfsimilarity through high-variability: Statistical analysis of Ethernet LAN traffic at the source level," IEEE/ACM Trans. Netw., vol. 5, no. 1, pp. 71-86, Feb. 1997. https://doi.org/10.1109/90.554723
  5. X. K. Zou et al., "Can accurate predictions improve video straming in cellular networks?," in Proc. HotMobile, 2015, pp. 57-62.
  6. N. Bouten et al., "QoE-driven in-network optimization for adaptive video streaming based on packet sampling measurements," J. Comput. Netw., vol. 81, no. C, pp. 96-115, Apr. 2015. https://doi.org/10.1016/j.comnet.2015.02.007
  7. K. Miller, A.-K. Al-Tamimi, and A. Wolisz, "Low-delay adaptive video streaming based on short-term TCP throughput prediction," Telecommunication Networks Group, Feb. 2015.
  8. S. Alcock and R. Nelson, "Application flow control in YouTube video streams," ACM SIGCOMM Comput. Commun. Review, vol. 41, no. 2, pp. 24-30, Apr. 2011. https://doi.org/10.1145/1971162.1971166
  9. T. Stockhammer, "Dynamic adaptive streaming over HTTP: Standards and design principles," in Proc. ACM MMSys, Feb. 2011, pp. 133-144.
  10. "HTTP live streaming resources," Apple developer. [Online]. Available: https://developer.apple.com/streaming/.
  11. "Microsoft smooth streaming," [Online]. Available: http://www.iis.net/downloads/smooth-streaming.
  12. "Adobe HTTP dynamic streaming," [Online]. Available: http://www.adobe.com/kr/products/hds-dynamic-streaming.html.
  13. C. Muller and C. Timmerer, "A VLC media player plugin enabling dynamic adaptive streaming over HTTP," in Proc. ACM MM, Nov. 2011, pp. 723-726.
  14. T.-Y. Huang, R. Johari, and N. McKeown, "Downton abbey without the hiccups: Buffer-based rate adaptation for HTTP video streaming," in Proc. ACM SIGCOMM Workshop on FHMN, Aug. 2013, pp. 9-14.
  15. F. Wamser et al., "Using buffered playtime for QoE-oriented resource management of YouTube video streaming," Trans. Emerging Telecommun. Tech., vol. 24, no. 3, pp. 288-302, Apr. 2013. https://doi.org/10.1002/ett.2636
  16. T.-Y. Huang, R. Johari, N. McKeown, M. Trunnell, and M.Watson, "Using the buffer to avoid rebuffers: Evidence from a large video streaming service," CoRR, pp. 1-13, Jan. 2014.
  17. Z. Li et al., "Probe and adapt: Rate adaptation for HTTP video streaming at scale," IEEE J. Sel. Areas Commun., vol. 32, no. 4, pp. 719-733, 2014. https://doi.org/10.1109/JSAC.2014.140405
  18. J. Jiang, V. Sekar, and H. Zhang, "Improving fairness, efficiency, and stability in HTTP-based adaptive video streaming with festive," in Proc. ACM CoNEXT, Dec. 2012, pp. 97-108.
  19. J. Famaey et al., "On the merits of SVC-based HTTP adaptive streaming," in Proc. IFIP/IEEE Int. Symp. Integrated Netw. Management, May 2013, pp. 419-426.
  20. T. Y. Huang, N. Handigol, B. Heller, N. McKeown, and R. Johari, "Confused, timid, and unstable: Picking a video streaming rate is hard," in Proc. ACM IMC, Nov. 2012, pp. 225-238.
  21. R. Houdaille and S. Gouache, "Shaping HTTP adaptive streams for a better user experience," in Proc. ACM MMSys, Feb. 2012, pp. 1-9.
  22. A. Lazaris and P. Koutsakis, "Modeling multiplexed traffic from H. 264/AVC videoconference streams," Comput. Commun., vol. 33, no. 10, pp. 1235-1242, June 2010. https://doi.org/10.1016/j.comcom.2010.03.014
  23. A. Biernacki, "Multi-scale modelling of VoIP traffic by MMPP," in Innovative Algorithms and Techniques in Automation, Industrial Electron. and Telecommun., Springer, 2007, pp. 55-60.
  24. P. Ameigeiras, J. J. Ramos-Munoz, J. Navarro-Ortiz, and J. M. Lopez-Soler, "Analysis and modelling of YouTube traffic," Trans. Emerging Telecommun. Tech., vol. 23, no. 4, pp. 360-377, June 2012. https://doi.org/10.1002/ett.2546
  25. X. Cheng, C. Dale, and J. Liu, "Statistics and social network of YouTube videos," in Proc. IWQoS, June 2008, pp. 229-238.
  26. A. Abhari and M. Soraya, "Workload generation for YouTube," Multimedia Tools and Applications, vol. 46, no. 1, pp. 91-118, Jan. 2010. https://doi.org/10.1007/s11042-009-0309-5
  27. A. Rao et al., "Network characteristics of video streaming traffic," in Proc. ACM CoNEXT, Dec. 2011, pp. 1-12.
  28. J. J. Ramos-Munoz, J. Prados-Garzon, P. Ameigeiras, J. Navarro-Ortiz, and J. M. Lopez-Soler, "Characteritics of mobile YouTube traffic," IEEE Wireless Commun., vol. 21, no. 1, pp. 18-25, Feb. 2014. https://doi.org/10.1109/MWC.2014.6757893
  29. J. Martin, Y. Fu, N.Wourms, and T. Shaw, "Characterizing Netflix bandwidth consumption," in Proc. IEEE CCNC, Jan. 2013, pp. 230-235.
  30. B. J. Villa and P. E. Heegaard, "Detecting period and burst durations in video streaming by means of active probing," Int. J. Comput. and Commun. Engineering, vol. 2, pp. 460-467, 2013.
  31. K. Oida, "Propagation of low variability in video traffic," J. Netw., vol. 10, no. 8, pp. 448-461, Aug. 2015.
  32. M. S. Taqqu and J. B. Levy, "Using renewal processes to generate longrange dependence and high variability," Dependence in Probability and Statistics, vol. 11, pp. 73-89, 1986.
  33. S. Akhshabi, A. C. Begen, and C. Dovrolis, "An experimental evaluation of rate-adaptation algorithms in daptive streaming over HTTP," in Proc. ACM MMSys, Feb. 2011, pp. 157-168.
  34. S. Akhshabi, L. Anantakrishnan, C. Dovrolis, and A. C. Begen, "What happens when HTTP adaptive streaming players compete for bandwidth?," in Proc. NOSSDAV, June 2012, pp. 9-14.
  35. S. Hemminger, "Network emulation with NetEm," in Linux Conf Au, Apr. 2005, pp. 18-23.
  36. Apache Web Server. [Online]. Available: http://www.apache.org/.
  37. A. Botta, A. Dainotti, and A. Pescape, "A tool for the generation of realistic network workload for emerging networking scenarios," Comput. Netw., vol. 56, no. 15, pp. 3531-3547, Oct. 2012. https://doi.org/10.1016/j.comnet.2012.02.019
  38. S. Lederer, C. Muller, and C. Timmerer, "Dynamic adaptive streaming over HTTP dataset," in Proc. ACM MMSys, Feb. 2012, pp. 89-94.
  39. C. Kreuzberger, D. Posch, and H. Hellwagner, "A scalable video coding dataset and toolchain for dynamic adaptive streaming over HTTP," in Proc. ACM MMSys, Mar. 2015, pp. 213-218.
  40. V. Jacobson, C. Leres, and S. McCanne, "TCPDUMP public repository," 2015.
  41. K. Park, G. Kim, and M. E. Crovella, "Effect of traffic self-similarity on network performance," in Proc. Voice, Video, and Data Commun., Oct. 1997, pp. 296-310.
  42. J. Liebeherr, A. Burchard, and F. Ciucu, "Delay bounds in communication networks with heavy-tailed and self-similar traffic," IEEE Trans. Inf. Theory, vol. 58, no. 2, pp. 1010-1024, Feb. 2012. https://doi.org/10.1109/TIT.2011.2173713
  43. G. Rangarajan and M. Ding, "An integrated approach to the assessment of long range correlation in time series data," Phys. Review E, vol. 61, pp. 4991-5001, 2000. https://doi.org/10.1103/PhysRevE.61.4991
  44. O. Diethelm and B. Wurtz, "Rmetrics," 2015, [Online]. Available: https://www.rmetrics.org/.
  45. R Core Team, R: A Language and Environment for Statistical Computing, 2015.
  46. N. Blagus, L. Subelj, and M. Bajec, "Self-similar scaling of density in complex real-world networks," Physica A: Statistical Mechanics and Its Applications, vol. 391, no. 8, pp. 2794-2802, Apr. 2012. https://doi.org/10.1016/j.physa.2011.12.055
  47. J. Kwapien and S. Drozdz, "Physical approach to complex systems," Physics Reports, vol. 515, no. 3, pp. 115-226, June 2012. https://doi.org/10.1016/j.physrep.2012.01.007
  48. R.Mantegna, "Hierarchical structure in financial markets," The European Physical Journal B, vol. 11, no. 1, pp. 193-197, Sept. 1999.
  49. R. C. Prim, "Shortest connection networks and some generalizations," Bell Labs Technical Journal, vol. 36, no. 6, pp. 1389-1401, Nov. 1957. https://doi.org/10.1002/j.1538-7305.1957.tb01515.x
  50. L. Qiu, Y. Zhang, and S. Keshav, "Understanding the performance of many TCP flows," Comput. Netw., vol. 37, no. 3, pp. 277-306, Nov. 2001. https://doi.org/10.1016/S1389-1286(01)00203-1