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Vol: 52(66) No: 2 / June 2007 Robust and Optimal Control of Packet Loss Probability for MPLS VPN Services with a Reactive Estimator Dongli Zhang School of Information Technology and Engineering, University of Ottawa, 161 Louis Pasteur, P.O. Box 450, Stn. A, Colonel By Hall, Room B-306, Ottawa, Ontario K1N 6N5, Canada, phone: +1 (613) 562-5800 ex, e-mail: dzhang@site.uottawa.ca, web: http://www.ncct.uottawa.ca Dan Ionescu School of Information Technology and Engineering, University of Ottawa, 161 Louis Pasteur, P.O. Box 450, Stn. A, Colonel By Hall, Room B-306, Ottawa, Ontario K1N 6N5, Canada, e-mail: ionescu@site.uottawa.ca, web: http://www.ncct.uottawa.ca Keywords: Packet Loss Probability, Estimation, Large Deviation Theory, VPN service, Robust, LMI. Abstract Provisioning QoS for the MPLS VPN services over packet switched networks is increasingly important for network service providers. To provide QoS, prior works usually adopted only the proactive service admission approach. It is still an open issue to control QoS parameters after the service has been instantiated. In this type of feedback control system, the key components include system model, transducer and controller. The dynamic packet loss system has been identified in the previous study. However, the identified linear model contains time-varying and norm-bounded uncertain parameters. This paper tries to construct a complete dynamic control system to robust and optimally control the packet loss probability. A reactive packet loss probability estimator is proposed as the transducer. A robust and optimal controller is then designed for the identified system model, where a Linear Matrix Inequality (LMI) approach for designing the Proportional-Integral (PI) controller is studied. By a number of experiments, the transient and steady state performance of the control system is evaluated on the live NCIT*net network. References [1] E. Rosen and Y. Rekhter, BGP/MPLS VPNs. www.ietf.org, 1999. [2] A. Parekh and R. Gallager, “A generalized processor sharing approach to flow control in integrated services networks: The single node case,” IEEE/ACM Transactions on Networking, vol. 1, pp. 137–150, 1993. [3] J. Qiu and E. Knightly, “Measurement-based admission control with aggregate traffic envelopes,” IEEE Transactions in Networking, vol. 9, no. 2, 2001. [4] L. Breslau and S. Jamin, “Comments on the performance of measurement-based admission control algorithms,” in INFOCOM’2000, 2000. [5] L. Benmohamed and S. M. Meerkov, “Feedback Control of Congestion In Packet Switching Networks: The case of a single congested node,” IEEE/ACM Transactions in Networking, vol. 1, pp. 693–708, 1993. [6] F. Blanchini, R. Cigno, and R. Tempo, “Robust Rate Control for Integrated Service Packet Networks,” IEEE/ACM Transactions in Networking, vol. 5, pp. 644–652, 2002. [7] A. Kolarov and G. Ramamurthy, “A Control Theoretic Approach to the Design of an Explicit Rate Controller for ABR Service,” IEEE/ACM Transactions on Networking, vol. 7, pp. 741–753, 1999. [8] M. Grossglauser and N. Tse, “A framework for robust measurement based admission control,” IEEE Transactions in Networking, vol. 7, no. 3, 1999. [9] C. Chang, Performance Guarantees In Communication Networks. Springer-Verlag, 2000. [10] D. Zhang and D. Ionescu, “Recursive parameter estimation for the dynamic packet loss system,” in Globecom 2005, St. Louis, USA. [11] J. Roberts and all. (eds), Broadband Network Teletraffic – Performance Evaluation and Design of Broadband Multiservice Networks. Springer-Verlag LNCS 1155, 1996. [12] D. Zhang and D. Ionescu, “On packet loss estimation for virtual private networks services,” in ICCCN 2004, Chicago, IL, USA. [13] L. C. Ludeman, Random Process Filtering, Estimation, and Detecion. John wiley, 2003. [14] kemin Zhou and J. C. Doyle, Essentials of Robust Control. Prentice Hall, 1998. [15] S. Boyd, L. E. Ghaoui, E. Feron, and V. Balakrishnan, Linear Matrix Inequalities in Systems an Control Theory. SIAM books, Philadelphia, 1994. [16] S. Boyd, “A note on parametric and nonparametric uncertainties in control systems,” Proc. American Control Conf., pp. 1847–1849, 1986. [17] D. M. Etter, Introduction to Matlab 7. Pearson eduction, 2005. [18] Y. Nesterov and A. Nemirovski, Interior Point Polynomial methods in Convex Programming: Theory and Applications. SIAM books, Philadelphia, 1994. [19] B. I. et al., “A testbed and research network for next generation services over next generation networks,” in Tridentcom 2005, 2005. [20] NCCT, http://www.ncct.uottawa.ca. |