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Characterizing a Meta-CDN

  • Oliver Hohlfeld
  • Jan Rüth
  • Konrad Wolsing
  • Torsten Zimmermann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10771)

Abstract

CDNs have reshaped the Internet architecture at large. They operate (globally) distributed networks of servers to reduce latencies as well as to increase availability for content and to handle large traffic bursts. Traditionally, content providers were mostly limited to a single CDN operator. However, in recent years, more and more content providers employ multiple CDNs to serve the same content and provide the same services. Thus, switching between CDNs, which can be beneficial to reduce costs or to select CDNs by optimal performance in different geographic regions or to overcome CDN-specific outages, becomes an important task. Services that tackle this task emerged, also known as CDN broker, Multi-CDN selectors, or Meta-CDNs. Despite their existence, little is known about Meta-CDN operation in the wild. In this paper, we thus shed light on this topic by dissecting a major Meta-CDN. Our analysis provides insights into its infrastructure, its operation in practice, and its usage by Internet sites. We leverage PlanetLab and Ripe Atlas as distributed infrastructures to study how a Meta-CDN impacts the web latency.

Notes

Acknowledgments

This work has been funded by the DFG as part of the CRC 1053 MAKI. We would like to thank Dean Robinson (Univ. Michigan) for his early contributions to the Alexa analysis, our shepherd Ignacio Castro and the anonymous reviewers.

References

  1. 1.
  2. 2.
  3. 3.
    Cisco umbrella list of top 1M domains. http://s3-us-west-1.amazonaws.com/umbrella-static/index.html
  4. 4.
  5. 5.
    Dyn DNS Traffic Steering. http://dyn.com
  6. 6.
    FORTINET FortiDirector. http://xdn.com/fd
  7. 7.
    Adhikari, V.K., Guo, Y., Hao, F., Hilt, V., Zhang, Z.L.: A tale of three CDNs: an active measurement study of Hulu and its CDNs. In: IEEE INFOCOM Workshops (2012)Google Scholar
  8. 8.
    Calder, M., Flavel, A., Katz-Bassett, E., Mahajan, R., Padhye, J.: Analyzing the performance of an Anycast CDN. In: ACM IMC (2015)Google Scholar
  9. 9.
    Chen, F., Sitaraman, R.K., Torres, M.: End-user mapping: next generation request routing for content delivery. In: ACM SIGCOMM (2015)Google Scholar
  10. 10.
    Dobrian, F., Sekar, V., Awan, A., Stoica, I., Joseph, D., Ganjam, A., Zhan, J., Zhang, H.: Understanding the impact of video quality on user engagement. In: ACM SIGCOMM (2011)Google Scholar
  11. 11.
    Flavel, A., Mani, P., Maltz, D., Holt, N., Liu, J., Chen, Y., Surmachev, O.: FastRoute: a scalable load-aware anycast routing architecture for modern CDNs. In: USENIX NSDI (2015)Google Scholar
  12. 12.
    Frank, B., Poese, I., Lin, Y., Smaragdakis, G., Feldmann, A., Maggs, B., Rake, J., Uhlig, S., Weber, R.: Pushing CDN-ISP collaboration to the limit. ACM SIGCOMM CCR 43(3) (2013)Google Scholar
  13. 13.
    Gerber, A., Doverspike, R.: Traffic types and growth in backbone networks. In: IEEE OFC/NFOEC (2011)Google Scholar
  14. 14.
    Labovitz, C., Iekel-Johnson, S., McPherson, D., Oberheide, J., Jahanian, F.: Internet inter-domain traffic. In: ACM SIGCOMM (2010)Google Scholar
  15. 15.
    Liu, H.H., Wang, Y., Yang, Y.R., Wang, H., Tian, C.: Optimizing cost and performance for content multihoming. In: ACM SIGCOMM (2012)Google Scholar
  16. 16.
    Mukerjee, M.K., Bozkurt, I.N., Maggs, B., Seshan, S., Zhang, H.: The impact of brokers on the future of content delivery. In: ACM HotNets (2016)Google Scholar
  17. 17.
    Mukerjee, M.K., Bozkurt, I.N., Ray, D., Maggs, B.M., Seshan, S., Zhang, H.: Redesigning CDN-broker interactions for improved content delivery. In: ACM CoNEXT (2017)Google Scholar
  18. 18.
    Nygren, E., Sitaraman, R.K., Sun, J.: The Akamai network: a platform for high-performance internet applications. SIGOPS OS Rev. 44(3), 2–19 (2010)CrossRefGoogle Scholar
  19. 19.
    Otto, J.S., Sánchez, M.A., Rula, J.P., Bustamante, F.E.: Content delivery and the natural evolution of DNS: remote DNS trends, Performance issues and alternative solutions. In: ACM IMC (2012)Google Scholar
  20. 20.
    Poese, I., Frank, B., Ager, B., Smaragdakis, G., Feldmann, A.: Improving content delivery using provider-aided distance information. In: ACM IMC (2010)Google Scholar
  21. 21.
    Su, A.-J., Choffnes, D.R., Kuzmanovic, A., Bustamante, F.E.: Drafting behind Akamai: inferring network conditions based on CDN redirections. IEEE/ACM ToN 17(6), 1752–1765 (2009)CrossRefGoogle Scholar
  22. 22.
    van Rijswijk-Deij, R., Jonker, M., Sperotto, A., Pras, A.: A high-performance, scalable infrastructure for large-scale active DNS measurements. IEEE JSAC 34(6), 1877–1888 (2016)Google Scholar
  23. 23.
    Xue, J., Choffnes, D., Wang, J.: CDNs meet CN an empirical study of CDN deployments in China. IEEE Access 5, 5292–5305 (2017)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Oliver Hohlfeld
    • 1
  • Jan Rüth
    • 1
  • Konrad Wolsing
    • 1
  • Torsten Zimmermann
    • 1
  1. 1.Communication and Distributed SystemsRWTH Aachen UniversityAachenGermany

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