Increasing the Coverage of Vantage Points in Distributed Active Network Measurements by Crowdsourcing

  • Valentin Burger
  • Matthias Hirth
  • Christian Schwartz
  • Tobias Hoßfeld
  • Phuoc Tran-Gia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8376)

Abstract

Internet video constitutes more than half of all consumer traffic. Most of the video traffic is delivered by content delivery networks (CDNs). The huge amount of traffic from video CDNs poses problems to access providers. To understand and monitor the impact of video traffic on access networks and the topology of CDNs, distributed active measurements are needed. Recently used measurement platforms are mainly hosted in National Research and Education Networks (NRENs). However, the view of these platforms on the CDN is very limited, since the coverage of NRENs is low in developing countries. Furthermore, campus networks do not reflect the characteristics of end user access networks. We propose to use crowdsourcing to increase the coverage of vantage points in distributed active network measurements. In this study, we compare measurements of a global CDN conducted in PlanetLab with measurements assigned to workers of a crowdsourcing platform. Thus, the coverage of vantage points and the sampled part of the global video CDN are analyzed. Our results show that the capability of PlanetLab to measure global CDNs is rather low, since the vast majority of requests is directed to the US. By using a crowdsourcing platform we obtain a diverse set of vantage points that reveals more than twice as many autonomous systems deploying video servers.

References

  1. 1.
    Adhikari, V., Jain, S., Chen, Y., Zhang, Z.: Vivisecting YouTube: An Active Measurement Study. In: Proceedings IEEE INFOCOM (2012)Google Scholar
  2. 2.
    Adhikari, V., Jain, S., Zhang, Z.: Where Do You “Tube”? Uncovering YouTube Server Selection Strategy. In: IEEE ICCCN (2011)Google Scholar
  3. 3.
    Bischof, Z.S., Otto, J.S., Sánchez, M.A., Rula, J.P., Choffnes, D.R., Bustamante, F.E.: Crowdsourcing isp characterization to the network edge. In: Proceedings of the First ACM SIGCOMM Workshop on Measurements Up the Stack (2011)Google Scholar
  4. 4.
    Cisco: Forecast and Methodology, 2012–2017. Cisco Visual Networking Index (2013)Google Scholar
  5. 5.
    Google: Peering & Content Delivery, https://peering.google.com/
  6. 6.
    Hirth, M., Hoßfeld, T., Tran-Gia, P.: Anatomy of a Crowdsourcing Platform - Using the Example of Microworkers.com. In: Workshop on Future Internet and Next Generation Networks (FINGNet), Seoul, Korea (2011)Google Scholar
  7. 7.
    Hoßfeld, T., Hausheer, D., Hecht, F., Lehrieder, F., Oechsner, S., Papafili, I., Racz, P., Soursos, S., Staehle, D., Stamoulis, G.D., Tran-Gia, P., Stiller, B.: An Economic Traffic Management Approach to Enable the TripleWin for Users, ISPs, and Overlay Providers. IOS Press Books Online, Towards the Future Internet - A European Research Perspective (2009)Google Scholar
  8. 8.
    Hoßfeld, T., Hirth, M., Tran-Gia, P.: Modeling of Crowdsourcing Platforms and Granularity of Work Organization in Future Internet. In: Proceedings of the International Teletraffic Congress, ITC (2011)Google Scholar
  9. 9.
    Hoßfeld, T., Schatz, R., Biersack, E., Plissonneau, L.: Internet Video Delivery in YouTube: From Traffic Measurements to Quality of Experience. In: Biersack, E., Callegari, C., Matijasevic, M. (eds.) Data Traffic Monitoring and Analysis. LNCS, vol. 7754, pp. 264–301. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  10. 10.
    InnoCentive, Inc.: Innocentive, http://www.innocentive.com/
  11. 11.
    Labovitz, C., Iekel-Johnson, S., McPherson, D., Oberheide, J., Jahanian, F.: Internet Inter-Domain Traffic. ACM SIGCOMM Computer Communication Review (2010)Google Scholar
  12. 12.
    MaxMind: GeoLite Databases, http://dev.maxmind.com/geoip/geolite/
  13. 13.
    PlanetLab: An open platform for developing, deploying, and accessing planetary-scale services, http://www.planet-lab.org/
  14. 14.
    Rafetseder, A., Metzger, F., Stezenbach, D., Tutschku, K.: Exploring youtube’s content distribution network through distributed application-layer measurements: a first view. In: Proceedings of the 2011 International Workshop on Modeling, Analysis, and Control of Complex Networks (2011)Google Scholar
  15. 15.
    Torres, R., Finamore, A., Kim, J.R., Mellia, M., Munafo, M.M., Rao, S.: Dissecting Video Server Selection Strategies in the YouTube Cdn. In: 31st International Conference on Distributed Computing Systems, ICDCS (2011)Google Scholar
  16. 16.
    Tran-Gia, P., Hoßfeld, T., Hartmann, M., Hirth, M.: Crowdsourcing and its Impact on Future Internet Usage. it - Information Technology 55 (2013)Google Scholar
  17. 17.
    Von Ahn, L., Maurer, B., McMillen, C., Abraham, D., Blum, M.: recaptcha: Human-based character recognition via web security measures. Science (5895) (2008)Google Scholar
  18. 18.
    Weblabcenter, Inc.: Microworkers, http://microworkers.com/

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Valentin Burger
    • 1
  • Matthias Hirth
    • 1
  • Christian Schwartz
    • 1
  • Tobias Hoßfeld
    • 1
  • Phuoc Tran-Gia
    • 1
  1. 1.Communication NetworksUniversity of WürzburgGermany

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