Wireless Ad Hoc and Sensor Networks

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Admission Controller Design for High-Speed Networks 171The CLR for the adaptive scheme is slightly higher than for adaptive withoverallocation and the PCR because the adaptive scheme is conservative;however, the CLR meets the target specification. The CLR resulting fromPCR is near zero as enough resources are a priori reserved. These resultsclearly show that the bandwidth assignment has to be accurate to admitnew sources. This chapter has demonstrated a novel scheme of assigningbandwidth, preventing congestion and a scheme for admitting sourcesbased on available capacity.4.7 ConclusionsThis chapter proposes a system theoretic approach to admission controllerdesign for high-speed networks. This admission controller explicitly computesthe equivalent bandwidth required to support each class of traffic,based on online observation of aggregate traffic flow obtained via NN-basedonline adaptive bandwidth estimation. The network buffer dynamics aremodeled as a nonlinear dynamical system and an adaptive NN algorithmis designed to estimate traffic flow. This adaptive NN approach does notrequire accurate information about network system dynamics or trafficrate. A novel weight tuning method was derived using rigorous mathematicalanalysis. In fact, the proposed adaptive estimator guarantees performance,as shown through the Lyapunov analysis.The bandwidth required to meet the QoS is then estimated and theavailable capacity is derived given the actual cell losses, latency, and theestimated traffic flow. This information, along with the intended PCR ofthe source to be admitted, predictive congestion indicator, and desiredQoS metrics, is used to construct the adaptive admission controller. Theadmission controller is expressed as a discrete-event state equation in“min”-“max” algebra and the dynamics of the buffer along with theadmission controller can be described as a hybrid system. The stability ofthe hybrid system is stated using nonsmooth Lyapunov function becausethe admission controller switches the operation of the buffer dynamics.The congestion and admission controllers operate at two different timescales.Results are presented to evaluate the performance of the proposedadmission controller with ON/OFF and bursty MPEG data. Based onthese results, it was concluded that the adaptive methodology generatesan accurate estimation of the bandwidth required and that needs to beassigned to the sources to meet the QoS. Accurately estimating the bandwidthonline along with the admission control will result in high networkutilization.
172 Wireless Ad Hoc and Sensor NetworksReferencesAntsaklis, P.J., Kohn, W., Nerode, A., and Sastry, S., Lecture Notes in ComputerScience: Hybrid Systems II, New York, Springer-Verlag, Vol. 99, 1995.Bae, J.J. and Suda, T., Survey of traffic control schemes and protocols in ATMnetworks, Proceedings of the IEEE, Vol. 79, No. 2, February 1991, pp. 170–189.Chen, B., Zhang, Y., Yen, J., and Zhao, W., Fuzzy adaptive connection admissioncontrol for real-time applications in ATM-based heterogeneous networks,Journal of Intelligent and Fuzzy Systems, Vol. 7, No. 2, 1999.Cheng, R.-G., Chang, C.-J., and Lin, L.-F., A QoS-provisioning neural fuzzy connectionadmission controller for multimedia high-speed networks, IEEE/ACM Transaction on Networking, Vol. 7, No. 1, 111–121, 1999.Dziong, Z., Juda, M., and Mason, L.G., A framework for bandwidth managementin ATM networks—aggregate equivalent bandwidth estimation approach,IEEE/ACM Transactions on Networking, Vol. 5, No. 1, 134–147, February 1997.Fahmy, S., Jain, R., Kalyanaraman, S., Goyal, R., Vandalore, B., On determiningthe fair bandwidth share for ABR connections in ATM networks, Proceedingsof the IEEE on Communications, Vol. 3, 1998, pp. 1485–1491.Gurin, R., Ahmadi, H., and Naghshineh, M., Equivalent capacity and its applicationto bandwidth allocation in high-speed networks, IEEE Journal on SelectedAreas in Communications, Vol. 9, No. 7, 968–981, September 1991.Jagannathan, S. and Talluri, J., Adaptive predictive congestion control of highspeednetworks, IEEE Transactions on Broadcasting, Vol. 48, No. 2, 129–139,June 2002.Jagannathan, S., Admission control design for high speed networks: a hybridsystem approach, Journal of High Speed Networks, Vol. 14, 263–281, 2005.Jagannathan, S., Neural Network Control of Nonlinear Discrete-Time Systems, Taylorand Francis (CRC), Boca Raton, FL, 2006.Jain, R., Congestion control and traffic management in ATM networks: recentadvances and a survey, Computer Networks and ISDN Systems, Vol. 28,1723–1738, 1996.Jamin, S., Danzig, P.B., Shenker, S.J., Zhang, L., A measurement-based admissioncontrol algorithm for integrated service packet networks, IEEE/ACM Transactionson Networking, Vol. 5, No. 1, February 1997.Lakshman, T.V., Mishra, P.P., and Ramakrishan, K.K., Transporting compressedvideo over ATM networks with explicit-rate feedback control, IEEE/ACMTransactions on Networking, Vol. 7, No. 5, October 1999.Liew, S. and Yin, D.C., A control-theoretic approach to adopting VBR compressedvideo for transport over a CBR communications channel, IEEE/ACM Transactionson Networking, Vol. 6, No. 1, 42–55, February 1998.Peng, M., Jagannathan, S., and Subramanya, S., End to end congestion control ofmultimedia high speed Internet, Journal of High Speed Networks, to appearin 2006.Ramadge, P.J. and Wonham, W.M., The control of discrete event systems, Proceedingsof the IEEE, Vol. 77, No. 1, 1989, pp. 81–89.
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18. Chaos in Automatic Control, edi
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CRC PressTaylor & Francis Group6000
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Table of Contents1 Background on Ne
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3 Congestion Control in ATM Network
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5.4.2 Distributed Power Controller
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7.3 Adaptive and Distributed Fair S
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9.2.2 Overview.....................
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the number of connections in each l
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y Maheswaran Thiagarajan, and tirel
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2 Wireless Ad Hoc and Sensor Networ
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10 Wireless Ad Hoc and Sensor Netwo
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2BackgroundIn this chapter, we prov
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Background 51u(k)x n (k + 1)x n (k)
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Background 53with x( 0)being the in
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Background 55with tr(.) the matrix
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Background 57nGiven a function ft (
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Background 592.3.2 Lyapunov Stabili
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Background 61as well as Jagannathan
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Background 63The subsequent example
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Background 65Evaluating this along
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Background 67Now, select the feedba
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Background 69is SISL if and only if
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Background 71L 1 (x)L(x, t)L 0 (x)0
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Background 73for some R > 0 such th
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Background 75Evaluating the first d
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Background 77Section 2.4Problem 2.4
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100 Wireless Ad Hoc and Sensor Netw
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7Distributed Fair Scheduling in Wir
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Distributed Fair Scheduling in Wire
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8Optimized Energy and Delay-Based R
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10Adaptive and Probabilistic Power
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