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Towards Optimal Placement of Monitoring Units in Time-Varying Networks Under Centralized Control

  • Sounak KarEmail author
  • Rhaban Hark
  • Amr Rizk
  • Ralf Steinmetz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10740)

Abstract

The increasing penetration of software-defined communication networks with centralized control has made network management a highly demanding task. Common monitoring approaches in the context of such convoluted high-speed networks have become a serious challenge in terms of complexity and resource management. Management functions rely on monitoring information such as the flow size distribution (FSD), to perform crucial activities such as load balancing and resource provisioning. In this paper, we propose a solution as to how one can utilize limited monitoring resources to estimate the FSD for distinct flows characterized by origin-destination pairs. We provide a method to dynamically adapt placement of monitoring units with some extracted knowledge about the change in FSD’s with time.

Notes

Acknowledgments

This work has been funded in parts by the German Research Foundation (DFG) as part of project B4 within the Collaborative Research Center (CRC) 1053 – MAKI. This work has been performed in parts in the framework of the CELTIC EUREKA project SENDATE-PLANETS (Project ID C2015/3-1), and it is partly funded by the German BMBF (Project ID 16KIS0471).

References

  1. 1.
    Benko, P., Veres, A.: A passive method for estimating end-to-end TCP packet loss. In: Proceedings of the IEEE GLOBECOM, vol. 3, pp. 2609–2613 (2002)Google Scholar
  2. 2.
    Papadogiannakis, A., Kapravelos, A., Polychronakis, M., Markatos, E.P., Ciuffoletti, A.: Passive end-to-end packet loss estimation for grid traffic monitoring. In: Proceedings of the CoreGRID Integration Workshop, pp. 79–93 (2006)Google Scholar
  3. 3.
    Friedl, A., Ubik, S., Kapravelos, A., Polychronakis, M., Markatos, E.P.: Realistic passive packet loss measurement for high-speed networks. In: Papadopouli, M., Owezarski, P., Pras, A. (eds.) TMA 2009. LNCS, vol. 5537, pp. 1–7. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-01645-5_1 CrossRefGoogle Scholar
  4. 4.
    Tootoonchian, A., Ghobadi, M., Ganjali, Y.: OpenTM: traffic matrix estimator for openflow networks. In: Krishnamurthy, A., Plattner, B. (eds.) PAM 2010. LNCS, vol. 6032, pp. 201–210. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-12334-4_21 CrossRefGoogle Scholar
  5. 5.
    Hark, R., Stingl, D., Richerzhagen, N., Nahrstedt, K., Steinmetz, R.: DistTM: collaborative traffic matrix estimation in distributed SDN control planes. In: Proceedings of the IFIP Networking, pp. 82–90 (2016)Google Scholar
  6. 6.
    Bozakov, Z., Rizk, A., Bhat, D., Zink, M.: Measurement-based flow characterization in centrally controlled networks. In: Proceedings of the IEEE INFOCOM (2016)Google Scholar
  7. 7.
    Shirali-Shahreza, S., Ganjali, Y.: FleXam: flexible sampling extension for monitoring and security applications in openflow. In: Proceedings of the ACM HotSDN, pp. 167–168 (2013)Google Scholar
  8. 8.
    Yu, M., Jose, L., Miao, R.: Software defined traffic measurement with OpenSketch. In: Proceedings of the USENIX NSDI, pp. 29–42 (2013)Google Scholar
  9. 9.
  10. 10.
    T-NOVA: Project. http://www.t-nova.eu/
  11. 11.
    SONATA: Project. http://www.sonata-nfv.eu/
  12. 12.
    Dietrich, D., Rizk, A., Papadimitriou, P.: AutoEmbed: Automated multi-provider virtual network embedding. In: Proceedings of ACM SIGCOMM 2013 (Demo track), pp. 465–466 (2013)Google Scholar
  13. 13.
    Houidi, I., Louati, W., Ben Ameur, W., Zeghlache, D.: Virtual network provisioning across multiple substrate networks. Comput. Netw. 55(4), 1011–1023 (2011)CrossRefzbMATHGoogle Scholar
  14. 14.
    Hohn, N., Veitch, D.: Inverting sampled traffic. In: Proceedings of the 3rd ACM SIGCOMM Conference on Internet Measurement. IMC 2003, New York, pp. 222–233. ACM (2003)Google Scholar
  15. 15.
    Tune, P., Veitch, D.: Fisher information in flow size distribution estimation. IEEE Trans. Inf. Theory 57(10), 7011–7035 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Benson, T., Akella, A., Maltz, D.A.: Network traffic characteristics of data centers in the wild. In: Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement. IMC 2010, New York, pp. 267–280. ACM (2010)Google Scholar
  17. 17.
    Roy, A., Zeng, H., Bagga, J., Porter, G., Snoeren, A.C.: Inside the social network’s (datacenter) network. SIGCOMM Comput. Commun. Rev. 45(4), 123–137 (2015)CrossRefGoogle Scholar
  18. 18.
    Yoon, S., Ha, T., Kim, S., Lim, H.: Scalable traffic sampling using centrality measure on software-defined networks. IEEE Commun. Mag. 55(7), 43–49 (2017)CrossRefGoogle Scholar
  19. 19.
    Tartakovsky, A.G., Moustakides, G.V.: State-of-the-art in Bayesian changepoint detection. Seq. Anal. 29(2), 125–145 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Karlin, S., Taylor, H.: A First Course in Stochastic Processes. Academic Press, New York (1975)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Sounak Kar
    • 1
    Email author
  • Rhaban Hark
    • 1
  • Amr Rizk
    • 1
  • Ralf Steinmetz
    • 1
  1. 1.Technische Universität DarmstadtDarmstadtGermany

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