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Assessing Affinity Between Users and CDN Sites

  • Xun FanEmail author
  • Ethan Katz-Bassett
  • John Heidemann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9053)

Abstract

Large web services employ CDNs to improve user performance. CDNs improve performance by serving users from nearby Front-End (FE) Clusters. They also spread users across FE Clusters when one is overloaded or unavailable and others have unused capacity. Our paper is the first to study the dynamics of the user-to-FE Cluster mapping for Google and Akamai from a large range of client prefixes. We measure how 32,000 prefixes associate with FE Clusters in their CDNs every 15 minutes for more than a month. We study geographic and latency effects of mapping changes, showing that 50–70 % of prefixes switch between FE Clusters that are very distant from each other (more than 1,000 km), and that these shifts sometimes (28–40 % of the time) result in large latency shifts (100 ms or more). Most prefixes see large latencies only briefly, but a few (2–5 %) see high latency much of the time. We also find that many prefixes are directed to several countries over the course of a month, complicating questions of jurisdiction.

Keywords

Mapping Change Server Selection Open Resolver Cluster Mapping Content Delivery Network 
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.

References

  1. 1.
    Ager, B., et al.: Web content cartography. In: ACM IMC (2011)Google Scholar
  2. 2.
    Calder, M., Fan, X., Hu, Z., Katz-Bassett, E., Heidemann, J., Govindan, R.: Mapping the expansion of google’s serving infrastructure. In: IMC, October 2013Google Scholar
  3. 3.
    Carter, R.L., Crovella, M.E.: Server selection using dynamic path characterization in wide-area networks. In: IEEE INFOCOM, April 1997Google Scholar
  4. 4.
    Casas, P., Fiadino, P., Bar, A.: Ip mining: extracting knowledge from the dynamics of the internet addressing space. In: ITC (2013)Google Scholar
  5. 5.
    Choffnes, D., Bustamante, F.E.: Taming the torrent: a practical approach to reducing cross-ISP traffic in peer-to-peer systems. In: ACM SIGCOMM (2008)Google Scholar
  6. 6.
    Crovella, M.E., Carter, R.L.: Dynamic server selection in the internet. In: IEEE HPCS, August 1995Google Scholar
  7. 7.
    Dilley, J., Maggs, B., Parikh, J., Prokop, H., Sitaraman, R., Weihl, B.: Globally distributed content delivery. IEEE Internet Comput. 6(5), 50–58 (2002)CrossRefGoogle Scholar
  8. 8.
    Edgerton, A.: NSA Spying allegations put google on hot seat in Brazil (2013). http://www.businessweek.com/news/2013-10-28/nsa-spying-allegations-put-google-on-hot-seat-corporate-brazil
  9. 9.
    Fan, X., Heidemann, J., Govindan, R.: Evaluating anycast in the domain name system. In: IEEE INFOCOM (2013)Google Scholar
  10. 10.
    Fan, X., Katz-Bassett, E., Heidemann, J.: Assessing affinity between users and CDN sites (extended). http://www.isi.edu/ xunfan/affinity_tech_report.pdf
  11. 11.
    Fiadino, P., D’Alconzo, A., Bar, A., Finamore, A., Casas, P.: On the detection of network traffic anomalies in content delivery network services. In: ITC (2014)Google Scholar
  12. 12.
    Fiadino, P., D’Alconzo, A., Casas, P.: Characterizing web services provisioning via cdns: the case of Facebook. In: TRAC (2014)Google Scholar
  13. 13.
    Finamore, A., Gehlen, V., Mellia, M., Munafò, M., Nicolini, S.: The need for an intelligent measurement plane: the example of time-variant cdn policies. In: IEEE NETWORKS (2012)Google Scholar
  14. 14.
    Guyton, J.D., Schwartz, M.F.: Locating nearby copies of replicated internet servers. In: ACM SIGCOMM, pp. 288–298, August 1995Google Scholar
  15. 15.
    Huang, C., Wang, A., Li, J., Ross, K.W.: Measuring and evaluating large-scale CDNs. Technical Report MSR-TR-2008-106, Microsoft Research, October 2008Google Scholar
  16. 16.
    Huang, Q., Birman, K., van Renesse, R., Lloyd, W., Kumar, S., Li, H.C.: An analysis of facebook photo caching. In: ACM SOSP (2013)Google Scholar
  17. 17.
    Krishnamurthy, B., Wills, C., Zhang, Y.: On the use and performance of content distribution networks. In: ACM IMW, pp. 169–182 (2001)Google Scholar
  18. 18.
    Krishnan, R., et al.: Moving beyond end-to-end path information to optimize CDN performance. In: ACM IMC (2009)Google Scholar
  19. 19.
    Mao, M., et al.: Peer-assisted content distribution in akamai netsession. In: ACM IMC, pp. 31–42 (2013)Google Scholar
  20. 20.
    Otto, J.S., et al.: Content delivery and the natural evolution of dns: remote dns trends, performance issues and alternative solutions. In: ACM IMC (2012)Google Scholar
  21. 21.
    Quan, L., Heidemann, J., Pradkin, Y.: When the Internet sleeps: correlating diurnal networks with external factors. In: ACM IMC (2014)Google Scholar
  22. 22.
    Robinson, F.: Google Sets Big belgian investment, April 2013. http://blogs.wsj.com/brussels/2013/04/10/google-sets-big-belgian-investment/
  23. 23.
    Higginbotham, S.: Akamai signs deal with opendns to make the web faster. http://gigaom.com/2014/06/03/akamai-signs-deal-with-opendns-to-make-the-web-faster/
  24. 24.
    Streibelt, F., Böttger, J., Chatzis, N., Smaragdakis, G., Feldmann, A.: Exploring EDNS-client-subnet adopters in your free time. In: ACM IMC (2013)Google Scholar
  25. 25.
    Su, A.-J., Choffnes, D.R., Kuzmanovic, A., Bustamante, F.E.: Drafting behind Akamai (Travelocity-based detouring). In: ACM SIGCOMM (2006)Google Scholar
  26. 26.
    Torres, R., Finamore, A., Kim, J.R., Mellia, M., Munafo, M.M., Rao, S.: Dissecting video server selection strategies in the Youtube CDN. In: ICDCS (2011)Google Scholar
  27. 27.
    Triukose, S., Wen, Z., Rabinovich, M.: Measuring a commercial content delivery network. In: ACM WWW, pp. 467–476 (2011)Google Scholar
  28. 28.
    Wendell, P., Jiang, J.W., Freedman, M.J., Rexford, J.: DONAR: decentralized server selection for cloud services. In: ACM SIGCOMM, August 2010Google Scholar
  29. 29.
    Wikipedia. Internet censorship by country. http://en.wikipedia.org/wiki/Internet_censorship_by_country
  30. 30.
  31. 31.
    Zhu, Y., Helsley, B., Rexford, J., Siganporia, A., Srinivasan, S.: LatLong: diagnosing wide-area latency changes for CDNs. IEEE TNSM 9(1), September 2012Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  1. 1.Information Sciences InstituteUSCMarina Del ReyCalifornia
  2. 2.Computer Science DepartmentUSCMarina Del ReyCalifornia

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