Phillipa Gill
ABSTRACT
This paper presents a traffic characterization study of the popular video sharing service, YouTube. Over a three month period we observed almost 25 million transactions between users on an edge network and YouTube, including more than 600,000 video downloads. We also monitored the globally popular videos over this period of time.
In the paper we examine usage patterns, file properties, popularity and referencing characteristics, and transfer behaviors of YouTube, and compare them to traditional Web and media streaming workload characteristics. We conclude the paper with a discussion of the implications of the observed characteristics. For example, we find that as with the traditional Web, caching could improve the end user experience, reduce network bandwidth consumption, and reduce the load on YouTube's core server infrastructure. Unlike traditional Web caching, Web 2.0 provides additional meta-data that should be exploited to improve the effectiveness of strategies like caching.
- About the U of C. http://ucalgary.ca/about/.Google Scholar
- S. Acharya and B. Smith. An Experiment to Characterize Videos Stored on the Web. In Proc. SPIE/ACM MMCN, San Jose, USA, Jan. 1998.Google Scholar
- S. Acharya, B. Smith, and P. Parnes. Characterizing User Access to Videos on the World Wide Web. In Proc. SPIE/ACM MMCN, San Jose, USA, Jan. 2000.Google Scholar
- J. Almeida, J. Krueger, D. Eager, and M. Vernon. Analysis of Educational Media Server Workloads. In Proc. ACM NOSSDAV, Port Jefferson, USA, June 2001. Google ScholarDigital Library
- M. Ames and M. Naaman. Why We Tag: Motivations for Annotation in Mobile and Online Media. In Proc. ACM CHI, San Jose, USA, May 2007. Google ScholarDigital Library
- M. Arlitt and T. Jin. Workload Characterization of the 1998 World Cup Website. IEEE Network, 14(3), 2000. Google ScholarDigital Library
- M. Arlitt and C. Williamson. Internet Web Servers: Workload Characterization and Performance Implications. IEEE/ACM Trans. on Networking, 5(5), 1997. Google ScholarDigital Library
- L. Breslau, P. Cao, L. Fan, G. Phillips, and S. Shenker. Web Caching and Zipf-like Distributions: Evidence and Implications. In Proc. IEEE INFOCOM, New York, USA, Mar. 1999.Google ScholarCross Ref
- Bro. http://www.bro-ids.org.Google Scholar
- R. Bunt and J. Murphy. The Measurement of Locality and the Behaviour of Programs. Computer Journal, 27(3), 1984. Google ScholarDigital Library
- CBC. YouTube's Bride Wig Out Revealed as 'net seed' for Ad Campaign. CBC Arts, Feb. 2007.Google Scholar
- E. Chang, M. Davis, P. Schmitz, and S. Boll. Panel Discussion: Web 2.0 and Multimedia: Challenge, Hype, Synergy. In Proc. ACM MULTIMEDIA, Santa Barbara, USA, Oct. 2006.Google Scholar
- X. Cheng, C. Dale, and J. Liu. Understanding the Characteristics of Internet Short Video Sharing: YouTube as a Case Study. Technical Report arXiv:0707.3670v1 {cs.NI}, Cornell University, arXiv e-prints, July 2007.Google Scholar
- L. Cherkasova and M. Gupta. Analysis of Enterprise Media Server Workloads: Access Patterns, Locality, Content Evolution, and Rate of Change. IEEE/ACM Trans. on Networking, 12(5), 2004. Google ScholarDigital Library
- M. Chesire, A. Wolman, G. Voelker, and H. Levy. Measurement and Analysis of a Streaming Media Workload. In Proc. USITS, San Francisco, USA, Mar. 2001. Google ScholarDigital Library
- C. Costa, I. Cunha, A. Borges, C. Ramos, M. Rocha, J. Almeida, and B. Ribeiro-Neto. Analyzing Client Interactivity in Streaming Media. In Proc. WWW, NY, USA, May 2004. Google ScholarDigital Library
- M. Crovella and A. Bestavros. Self-Similarity in World Wide Web Traffic: Evidence and Possible Causes. IEEE/ACM Trans. on Networking, 5(6), 1997. Google ScholarDigital Library
- C. Cunha, A. Bestavros, and M. Crovella. Characteristics of World Wide Web Client-based Traces. Technical Report BUCS-TR-1995-010, Boston University, USA, Apr. 1995. Google ScholarDigital Library
- P. Denning. Working sets past and present. IEEE Trans. Software Eng., 6(1), 1980. Google ScholarDigital Library
- B. Duska, D. Marwood, and M. Freeley. The Measured Access Characteristics of World-Wide-Web Client Proxy Caches. In Proc. USITS, Monterey, USA, Mar. 1997. Google ScholarDigital Library
- Facebook. http://www.facebook.com.Google Scholar
- Flickr. http://www.flickr.com.Google Scholar
- FlixHunt. http://www.flixhunt.com.Google Scholar
- S. Gribble and E. Brewer. System Design Issues for Internet Middleware Services: Deductions from a Large Client Trace. In Proc. USITS, Monterey, USA, Mar. 1997. Google ScholarDigital Library
- L. Guo, E. Tan, S. Chen, Z. Xiao, O. Spatscheck, and X. Zhang. Delving into Internet Streaming Media Delivery: A Quality and Resource Utilization Perspective. In Proc. ACM IMC, Rio de Janeriro, Brazil, Oct. 2006. Google ScholarDigital Library
- M. Halvey and M. Keane. Exploring Social Dynamics in Online Media Sharing. In Proc. of WWW, Banff, Canada, May 2007. Google ScholarDigital Library
- N. Harel, V. Vellanki, A. Chervenak, G. Abowd, and U. Ramachandran. Characterizing A Media-Enhanced Classroom Server. In Proc. of IEEE Workshop on Workload Characterization (WCC), Austin, USA, Oct. 1999.Google Scholar
- M. Li, M. Claypool, R. Kinicki, and J. Nichols. Characteristics of streaming media stored on the web. ACM Trans. Inter. Tech., 5(4), 2005. Google ScholarDigital Library
- Business Intelligence Lowdown. Top 10 Largest Databases in the World, Feb. 2007.ÜGoogle Scholar
- A. Mahanti, D. Eager, and C. Williamson. Temporal Locality and its Impact on Web Proxy Cache Performance. Perform. Eval., 42(2--3), 2000. Google ScholarDigital Library
- A. Mahanti, C. Williamson, and D. Eager. Traffic Analysis of a Web Proxy Caching Hierarchy. IEEE Network, 14(3), 2000. Google ScholarDigital Library
- S. Majumdar and R. Bunt. Measurement and Analysis of Locality Phases in File Referencing Behaviour. In Proc ACM SIGMETRICS/PERFORMANCE, Raleigh, USA, June 1986. Google ScholarDigital Library
- J. Milani. Coming to Your Screen: DIY TV. BBC Money Programme, 2007.Google Scholar
- M. Musgrove. Viacom Decides YouTube Is a Foe. Washington Post, Feb. 2007.Google Scholar
- My Space. http://www.myspace.com.Google Scholar
- V. Paxson. Empirically-Derived Analytic Models of Wide-Area TCP Connections. IEEE/ACM Trans. on Net., 2(4), 1994. Google ScholarDigital Library
- P. Schmitz. Leveraging Community Annotations for Image Adaptation to Small Presentation Formats. In Proc. ACM MULTIMEDIA, Santa Barbara, USA, Oct. 2006. Google ScholarDigital Library
- K. Sripanidkulchai, B. Maggs, and H. Zhang. An Analysis of Live Streaming Workloads on the Internet. In Proc. ACM IMC, Taormina, Italy, Oct. 2004. Google ScholarDigital Library
- Tcpdump. http://www.tcpdump.org.Google Scholar
- USA Today. YouTube Serves up 100 million Videos a Day Online, July 2006.Google Scholar
- Wordpress. http://www.wordpress.com.Google Scholar
- YouTube. http://www.youtube.com.Google Scholar
- H. Yu, D. Zheng, B. Zhao, and W. Zheng. Understanding User Behavior in Large-Scale Video-on-Demand Systems. SIGOPS Oper. Syst. Rev., 40(4), 2006. Google ScholarDigital Library
- G. Zipf. Human Behavior and the Principle of Least Effort. Addison-Wesley (Reading MA), 1949.Google Scholar
Index Terms
- Youtube traffic characterization: a view from the edge
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