Abstract
In this paper we present OpenTM, a traffic matrix estimation system for OpenFlow networks. OpenTM uses built-in features provided in OpenFlow switches to directly and accurately measure the traffic matrix with a low overhead. Additionally, OpenTM uses the routing information learned from the OpenFlow controller to intelligently choose the switches from which to obtain flow statistics, thus reducing the load on switching elements. We explore several algorithms for choosing which switches to query, and demonstrate that there is a trade-off between accuracy of measurements, and the worst case maximum load on individual switches, i.e., the perfect load balancing scheme sometimes results in the worst estimate, and the best estimation can lead to worst case load distribution among switches. We show that a non-uniform distribution querying strategy that tends to query switches closer to the destination with a higher probability has a better performance compared to the uniform schemes. Our test-bed experiments show that for a stationary traffic matrix OpenTM normally converges within ten queries which is considerably faster than existing traffic matrix estimation techniques for traditional IP networks.
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References
Zhao, Q., Ge, Z., Wang, J., Xu, J.: Robust traffic matrix estimation with imperfect information: Making use of multiple data sources. SIGMETRICS Performance Evaluation Review 34(1), 133–144 (2006)
Vardi, Y.: Network tomography: Estimating source-destination traffic intensities from link data. Journal of the American Statistical Association 91(433), 365–377 (1996)
Nucci, A., Diot, C.: Design of IGP link weight changes for estimation of traffic matrices. In: Proceedings of the 2004 Conference on Computer Communications (2004)
Feldmann, A., Greenberg, A., Lund, C., Reingold, N., Rexford, J., True, F.: Deriving traffic demands for operational IP networks: Methodology and experience. IEEE/ACM Transactions on Networking 9(3), 265–280 (2001)
Papagiannaki, K., Taft, N., Lakhina, A.: A distributed approach to measure IP traffic matrices. In: Proceedings of the 4th ACM SIGCOMM Conference on Internet Measurement (2004)
Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. SIGCOMM Computer Communication Review 32(4), 161–174 (2002)
McKeown, N., Anderson, T., Balakrishnan, H., Parulkar, G., Peterson, L., Rexford, J., Shenker, S., Turner, J.: OpenFlow: Enabling innovation in campus networks. SIGCOMM Computer Communication Review 38(2), 69–74 (2008)
Gude, N., Koponen, T., Pettit, J., Pfaff, B., Casado, M., McKeown, N., Shenker, S.: NOX: Towards an operating system for networks. SIGCOMM Computer Communication Review 38(3), 105–110 (2008)
Medina, A., Fraleigh, C., Taft, N., Bhattacharrya, S., Diot, C.: A taxonomy of IP traffic matrices. In: SPIE ITCOM: Scalability and Traffic Control in IP Networks II, Boston (August 2002)
Pang, R., Allman, M., Bennett, M., Lee, J., Paxson, V., Tierney, B.: A first look at modern enterprise traffic. In: Proceedings of the 5th ACM SIGCOMM Conference on Internet Measurement, Berkeley, CA, USA, p. 2 (2005)
Naous, J., Erickson, D., Covington, G.A., Appenzeller, G., McKeown, N.: Implementing an OpenFlow switch on the NetFPGA platform. In: Franklin, M.A., Panda, D.K., Stiliadis, D. (eds.) ANCS, pp. 1–9. ACM, New York (2008)
Hemminger, S.: Network emulation with NetEm. In: Linux Conference, Australia (April 2005)
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Tootoonchian, A., Ghobadi, M., Ganjali, Y. (2010). OpenTM: Traffic Matrix Estimator for OpenFlow Networks. In: Krishnamurthy, A., Plattner, B. (eds) Passive and Active Measurement. PAM 2010. Lecture Notes in Computer Science, vol 6032. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12334-4_21
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DOI: https://doi.org/10.1007/978-3-642-12334-4_21
Publisher Name: Springer, Berlin, Heidelberg
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