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Application Bandwidth and Flow Rates from 3 Trillion Flows Across 45 Carrier Networks

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Passive and Active Measurement (PAM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 10176))

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Abstract

Geographically broad, application-aware studies of large subscriber networks are rarely undertaken because of the challenges of accessing secured network premises, protecting subscriber privacy, and deploying scalable measurement devices. We present a study examining bandwidth consumption and the rate at which new flows are created in 45 cable, DSL, cellular and WiFi subscriber networks across 26 countries on six continents. Using deep packet inspection, we find that one or two applications can strongly influence the magnitude and duration of daily bandwidth peaks. We analyze bandwidth over 7 days to better understand the potential for network optimization using virtual network functions. We find that on average cellular and non-cellular networks operate at 61% and 57% of peak bandwidth respectively. Since most networks are over provisioned, there is considerable room for optimization.

Our study of flow creation reveals that DNS is the top producer of new flows in 22 of the 45 networks (accounting for 20–61% of new flows in those networks). We find that peak flow rates (measured in thousands of flows per Gigabit) can vary by several orders of magnitude across applications. Networks whose application mix includes large proportions of DNS, PeerToPeer, and social networking traffic can expect to experience higher overall peak flow rates. Conversely, networks which are dominated by video can expect lower peak flow rates. We believe that these findings will prove valuable in understanding how traffic characteristics can impact the design, evaluation, and deployment of modern networking devices, including virtual network functions.

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References

  1. http://www.bittorrent.org/beps/bep_0029.html

  2. Akamai Technologies Inc., Akamai’s State of the Internet, vol. 7, no. 4, Q4 (2014)

    Google Scholar 

  3. Barford, P., Kline, J., Plonka, D., Ron, A.: A signal analysis of network traffic anomalies. In: 2nd ACM SIGCOMM Workshop on Internet Measurement (2002)

    Google Scholar 

  4. Bolla, R., Bruschi, R., Carrega, A., Davoli, F., Suino, D., Vassilakis, C., Zafeiropoulos, A.: Cutting the energy bills of internet service providers and telecoms through power management: an impact analysis. Comput. Netw. 56(10), 2320–2342 (2012)

    Article  Google Scholar 

  5. Bolla, R., Bruschi, R., Lombardo, C., Mangialardi, S.: Dropv2: energy efficiency through network function virtualization. IEEE Netw. 28(2), 26–32 (2014)

    Article  Google Scholar 

  6. Bolla, R., Bruschi, R., Lombardo, C., Suino, D.: Evaluating the energy-awareness of future Internet devices. In: IEEE Conference on High Performance Switching and Routing, July 2011

    Google Scholar 

  7. Butkiewicz, M., Madhyastha, H.V., Sekar, V.: Understanding website complexity: measurements, metrics, and implications. In: ACM IMC (2011)

    Google Scholar 

  8. Carela-Español, V., Barlet-Ros, P., Bifet, A., Fukuda, K.: A streaming flow-based technique for traffic classification applied to 12 + 1 years of Internet traffic. Telecommun. Syst. 63(2), 191–204 (2016)

    Article  Google Scholar 

  9. Castro, S., Zhang, M., John, W., Wessels, D., Claffy, K.: Understanding and preparing for DNS evolution. In: Ricciato, F., Mellia, M., Biersack, E. (eds.) TMA 2010. LNCS, vol. 6003, pp. 1–16. Springer, Heidelberg (2010). doi:10.1007/978-3-642-12365-8_1

    Chapter  Google Scholar 

  10. Christensen, K., Reviriego, P., Nordman, B., Bennett, M., Mostowfi, M., Maestro, J.A.: IEEE 802.3 az: the road to energy efficient ethernet. IEEE Commun. Mag. 48(11), 50–56 (2010)

    Article  Google Scholar 

  11. Cisco Systems Inc. The Zettabyte Era: Trends and Analysis, May 2015

    Google Scholar 

  12. Curtis, A.R., Mogul, J.C., Tourrilhes, J., Yalagandula, P., Sharma, P., Banerjee, S.: Devoflow: scaling flow management for high-performance networks. In: ACM SIGCOMM (2011)

    Google Scholar 

  13. Estan, C., Keys, K., Moore, D., Varghese, G.: Building a better NetFlow. In: ACM SIGCOMM (2004)

    Google Scholar 

  14. Fomenkov, M., Keys, K., Moore, D., Claffy, K.: Longitudinal study of internet traffic in 1998–2003. In: Winter International Symposium on Information and Communication Technologies (2004)

    Google Scholar 

  15. Fraleigh, C., Moon, S., Lyles, B., Cotton, C., Khan, M., Moll, D., Rockell, R., Seely, T., Diot, C.: Packet-level traffic measurements from the sprint IP backbone. IEEE Netw. Mag. 17(6), 6–16 (2003)

    Article  Google Scholar 

  16. Fukuda, K., Asai, H., Nagami, K.: Tracking the evolution and diversity in network usage of smartphones. In: ACM IMC (2015)

    Google Scholar 

  17. Han, B., Gopalakrishnan, V., Ji, L., Lee, S.: Network function virtualization: challenges and opportunities for innovations. IEEE Commun. Mag. 53(2), 90–97 (2015)

    Article  Google Scholar 

  18. Kaminsky, D.: Black ops 2008: It’s the end of the cache as we know it (2008). http://www.slideshare.net/dakami/dmk-bo2-k8

  19. Krishnan, S., Monrose, F.: DNS prefetching and its privacy implications: when good things go bad. In: USENIX Conference on Large-scale Exploits and Emergent Threats (2010)

    Google Scholar 

  20. Labovitz, C., Iekel-Johnson, S., McPherson, D., Oberheide, J., Jahanian, F.: Internet inter-domain traffic. In: ACM SIGCOMM (2010)

    Google Scholar 

  21. Mai, J., Chuah, C.-N., Sridharan, A., Ye, T., Zang, H.: Is sampled data sufficient for anomaly detection? In: ACM IMC (2006)

    Google Scholar 

  22. Maier, G., Feldmann, A., Paxson, V., Allman, M.: On dominant characteristics of residential broadband internet traffic. In: ACM IMC (2009)

    Google Scholar 

  23. Moore, D., Shannon, C., Brown, D.J., Voelker, G.M., Savage, S.: Inferring internet denial-of-service activity. ACM Trans. Comput. Syst. 24(2), 115–139 (2006)

    Article  Google Scholar 

  24. Nedevschi, S., Popa, L., Iannaccone, G., Ratnasamy, S., Wetherall, D.: Reducing network energy consumption via sleeping and rate-adaptation. In: NSDI (2008)

    Google Scholar 

  25. Richter, P., Chatzis, N., Smaragdakis, G., Feldmann, A., Willinger, W.: Distilling the internet’s application mix from packet-sampled traffic. In: Mirkovic, J., Liu, Y. (eds.) PAM 2015. LNCS, vol. 8995, pp. 179–192. Springer, Cham (2015). doi:10.1007/978-3-319-15509-8_14

    Google Scholar 

  26. Rotem, E., Naveh, A., Ananthakrishnan, A., Rajwan, D., Weissmann, E.: Power-management architecture of the Intel microarchitecture code-named Sandy Bridge. IEEE Micro 2(2), 20–27 (2012)

    Article  Google Scholar 

  27. Sandvine Inc., Global Internet Phenomena, December 2015. https://www.sandvine.com/trends/global-internet-phenomena/

  28. Thompson, K., Miller, G., Wilder, R.: Wide-area Internet traffic patterns and characteristics. IEEE Netw. 11(6), 10–23 (1997)

    Article  Google Scholar 

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Acknowledgments

Tim Brecht’s work was partially supported by a Natural Sciences and Engineering Research Council of Canada Discovery Grant. Thanks to Dan Deeth, Ian Wormsbecker, and Sau Cheng Lim at Sandvine for their assistance in gathering data, understanding network deployments, and for feedback on several drafts of this paper. We also thank Bernard Wong and S. Keshav from the University of Waterloo for their comments on an earlier version of this paper.

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Authors and Affiliations

  1. Sandvine Incorporated, Waterloo, Canada

    David Pariag

  2. Cheriton School of Computer Science, University of Waterloo, Waterloo, Canada

    Tim Brecht

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  1. David Pariag
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  2. Tim Brecht
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Correspondence to Tim Brecht .

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Editors and Affiliations

  1. CSIRO, Sydney, New South Wales, Australia

    Mohamed Ali Kaafar

  2. Queen Mary University of London, London, United Kingdom

    Steve Uhlig

  3. International Computer Science Institute, Berkeley, California, USA

    Johanna Amann

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Pariag, D., Brecht, T. (2017). Application Bandwidth and Flow Rates from 3 Trillion Flows Across 45 Carrier Networks. In: Kaafar, M., Uhlig, S., Amann, J. (eds) Passive and Active Measurement. PAM 2017. Lecture Notes in Computer Science(), vol 10176. Springer, Cham. https://doi.org/10.1007/978-3-319-54328-4_10

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  • DOI: https://doi.org/10.1007/978-3-319-54328-4_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54327-7

  • Online ISBN: 978-3-319-54328-4

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