Advertisement

Predicting Elephant Flows in Internet Exchange Point Programmable Networks

  • Marcus Vinicius Brito da SilvaEmail author
  • Arthur Selle JacobsEmail author
  • Ricardo José PfitscherEmail author
  • Lisandro Zambenedetti GranvilleEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 926)

Abstract

Internet Exchange Points (IXPs) are high-performance networks that allow multiple autonomous systems to exchange traffic, with benefits ranging from cost reductions to performance improvements. As in any network, IXP operators face daily management challenges to promote better usage of the services provided by the network. An essential problem in IXP management concerns the identification of elephant flows, which are characterized by having traffic size and duration significantly higher than other flows. The current approaches to the identification of elephant flow in IXP networks depend that the analyzed flows exceed predefined thresholds to classify them as elephants. However, although it is not perceptible initially, elephant flows are elephant ones since their first packet. Hence, in this paper, we present a mechanism to predict flows behavior using historical observations and, by recognizing temporal patterns, identify elephant flows even before they exceed such thresholds. Our approach consists in predicting new flows size and duration through a Locally Weighted Regression (LWR) model, using the previous flows behavior and its temporal correlation with the new flow. The experimental results show that our mechanism is able to predict the volume and duration of new flows, and react to elephant flows rapidly, approximately 50.3 ms with up to 32 historical samples in the prediction model. These numbers are much smaller than the time each flow would take to exceed the thresholds to classify it as an elephant. In addition, the mechanism accurately predicts up to 80% of elephant flows in our evaluation scenarios and approximately 5% of false positives.

Keywords

Software-defined networking Network management P4 language 

Notes

Acknowledgement

We thank CNPq for the financial support. This research has been supported by call Universal 01/2016 (CNPq), project NFV Mentor process 423275/2016-0.

References

  1. 1.
    Augustin, B., Krishnamurthy, B., Willinger, W.: IXPs: mapped? In: ACM SIGCOMM Conference on Internet Measurement, IMC 2009, pp. 336–349. ACM (2009)Google Scholar
  2. 2.
    Cardona Restrepo, J.C., Stanojevic, R.: IXP traffic: a macroscopic view. In: Proceedings of the 7th Latin American Networking Conference, pp. 1–8. ACM (2012)Google Scholar
  3. 3.
    Guo, L., Matta, I.: The war between mice and elephants. In: 2001 Ninth International Conference on Network Protocols, pp. 180–188. IEEE (2001)Google Scholar
  4. 4.
    Knob, L.A.D., Esteves, R.P., Granville, L.Z., Tarouco, L.M.R.: Mitigating elephant flows in SDN–based IXP networks. In: 2017 IEEE Symposium on Computers and Communications (ISCC), pp. 1352–1359. IEEE (2017)Google Scholar
  5. 5.
    Gregori, E., Improta, A., Lenzini, L., Orsini, C.: The impact of IXPs on the AS-level topology structure of the internet. In: Computer Communications, pp. 68–82. Elsevier (2011)Google Scholar
  6. 6.
    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 Conference on Internet Measurement, vol. 41, pp. 254–265. ACM (2011)Google Scholar
  7. 7.
    Suh, J., Kwon, T.T., Dixon, C., Felter, W., Carter, J.: OpenSample: a low-latency, sampling-based measurement platform for commodity SDN. In: 34th IEEE International Conference on Distributed Computing Systems (ICDCS), pp. 228–237. IEEE (2014)Google Scholar
  8. 8.
    Knob, L.A.D., Esteves, R.P., Granville, L.Z., Tarouco, L.M.R.: SDEFIX–identifying elephant flows in SDN-based IXP networks. In: IEEE/IFIP Network Operations and Management Symposium (NOMS), pp. 19–26. IEEE (2016)Google Scholar
  9. 9.
    sFlow: sFlow.org (2018). http://www.sflow.org
  10. 10.
    McKeown, N., Anderson, T., Balakrishnan, H., Parulkar, G., Peterson, L., Rexford, J., Shenker, S., Turner, J.: OpenFlow: enabling innovation in campus networks. In: ACM SIGCOMM Conference on Internet Measurement, pp. 69–74. ACM (2008)Google Scholar
  11. 11.
    da Silva, M.V.B., Jacobs, A.S., Pfitscher, R.J., Granville, L.Z.: IDEAFIX: identifying elephant flows in P4-based IXP networks. In: Proceedings of the IEEE Global Telecommunications Conference (GLOBECOM). IEEE (2018)Google Scholar
  12. 12.
    Bosshart, P., Daly, D., Gibb, G., Izzard, M., McKeown, N., Rexford, J., Schlesinger, C., Talayco, D., Vahdat, A., Varghese, G., et al.: P4: programming protocol-independent packet processors. In: ACM SIGCOMM Conference on Internet Measurement, pp. 87–95. ACM (2014)Google Scholar
  13. 13.
    Cleveland, W.S., Devlin, S.J.: Locally weighted regression: an approach to regression analysis by local fitting. J. Am. Stat. Assoc. 83(403), 596–610 (1988)CrossRefGoogle Scholar
  14. 14.
    Elattar, E.E., Goulermas, J., Wu, Q.H.: Electric load forecasting based on locally weighted support vector regression. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 40(4), 438–447 (2010)CrossRefGoogle Scholar
  15. 15.
    Zhang, Y., Breslau, L., Paxson, V., Shenker, S.: On the characteristics and origins of internet flow rates. In: ACM SIGCOMM Conference on Internet Measurement, vol. 32, no. 4, pp. 309–322. ACM, New York, August 2002Google Scholar
  16. 16.
    Mori, T., Kawahara, R., Naito, S., Goto, S.: On the characteristics of internet traffic variability: spikes and elephants. IEICE Trans. Inf. Syst. 87(12), 2644–2653 (2004)Google Scholar
  17. 17.
    Fang, W., Peterson, L.: Inter-AS traffic patterns and their implications. In: IEEE Global Telecommunications Conference (GLOBECOM), vol. 3, pp. 1859–1868. IEEE (1999)Google Scholar
  18. 18.
    Mori, T., Uchida, M., Kawahara, R., Pan, J., Goto, S.: Identifying elephant flows through periodically sampled packets. In: ACM SIGCOMM Conference on Internet Measurement, IMC 2004, pp. 115–120. ACM (2004)Google Scholar
  19. 19.
    IX Australia: Australia Internet Exchange Point (2018). https://www.ix.asn.au/
  20. 20.
    Internet Steering Committee in Brazil: Brazil Internet Exchange Points (2018). http://ix.br/trafego/agregado/rs
  21. 21.
    Li, Y., Liu, H., Yang, W., Hu, D., Wang, X., Xu, W.: Predicting inter-data-center network traffic using elephant flow and sublink information. IEEE Trans. Netw. Serv. Manag. 13(4), 782–792 (2016)Google Scholar
  22. 22.
    Schaal, S., Atkeson, C.G.: Robot juggling: implementation of memory-based learning. IEEE Control Syst. 14(1), 57–71 (1994)CrossRefGoogle Scholar
  23. 23.
    Jain, R.: The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation, and Modeling. Wiley, Hoboken (1990)Google Scholar
  24. 24.
    Simonoff, J.S.: Smoothing Methods in Statistics. Springer Science & Business Media, Berlin (2012)zbMATHGoogle Scholar
  25. 25.
    Wand, M.P., Schucany, W.R.: Gaussian-based kernels. Can. J. Stat. 18(3), 197–204 (1990)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Basat, R., Einziger, G., Friedman, R., Luizelli, M., Waisbard, E.: Constant time updates in hierarchical heavy hitter. In: ACM SIGCOMM Conference on Internet Measurement, SIGCOMM 2017, pp. 127–140. ACM (2017)Google Scholar
  27. 27.
    AMS-IX: Amsterdam Internet Exchange Infrastructure (2018). https://ams-ix.net/technical/ams-ix-infrastructure

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institute of InformaticsFederal University of Rio Grande do SulPorto AlegreBrazil

Personalised recommendations