Wireless Ad Hoc and Sensor Networks

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Congestion Control in ATM Networks and the Internet 139general, it is very difficult to use offline training as the bursty MPEG datais statistically different from one source to the other.Novel weight-tuning methods are derived for the NN. A rigorous mathematicalanalysis is provided for the closed-loop system, defined in terms oferror in buffer occupancy, unlike in other available congestion control techniques.In fact, the proposed NN controller guaranteed performance as shownthrough the Lyapunov analysis. In addition, the performance in terms of CLRcan be reduced to arbitrarily small values by choosing the gains appropriately.Results are presented by evaluating the performance of the proposedcontroller during congestion, when both ON/OFF and bursty MPEGsources were transmitting data onto the network. Based on the results, itwas concluded that the proposed methodology generates a low CLR andoverall delay compared to the adaptive ARMAX, one layer, and the thresholdingmethods, and the NN controller ensured fairness during congestion.In addition, in most cases, one can design a switch with a smallbuffer due to the weight adaptation. In other words, simulation resultsshow that the performance of the proposed two-layer NN-based congestioncontroller varies insignificantly with an increase in buffer size. Thisis an important result, which can be used to design buffers for networkswitches. Finally, the learning-based methodology provides an efficientqueue management for ATM networks.Next, this chapter discussed a rate-based end-to-end congestion controlscheme for packet switched networks. The buffer dynamics were modeledas a nonlinear dynamical system, which is driven by the outgoing bandwidth,bottleneck queue level, and inflow rate. The bottleneck queue levelis estimated by packet-arrival rate at ingress/egress nodes. A rigorousmathematical analysis is provided for the closed-loop system, defined interms of error in queue length unlike in other available congestion controltechniques. In fact, the proposed controller guaranteed performance, asshown through the Lyapunov analysis. Simulation examples were givento justify the theoretical conclusions. Based on the results, it was concludedthat the proposed methodology performs better compared to theconventional New-Reno TCP method.ReferencesBae, 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.Bansal, D. and Balakrishnan, H., Binomial congestion control algorithms, Proceedingsof the INFOCOM, Vol. 2, 2001, pp. 631–640.Benmohamed, L. and Meerkov, S.M., Feedback control of congestion in packetswitching networks: the case of a single congested node, IEEE/ACM Transactionon Networking, Vol. 1, No. 6, 693–707, 1993.
140 Wireless Ad Hoc and Sensor NetworksBonomi, F. and Fendick, K., The Rate-Based Flow Control Framework for the ABRATM Service, IEEE Network, Vol. 9, No. 2, 1995, pp. 25–39.Bonomi, F., Mitra, D. and Seery, J.B., Adaptive algorithms for feedback-based flowcontrol in high-speed, wide-area ATM networks, IEEE Journal on SelectedAreas in Communications, Vol. 13, No. 7, 1267–1283, September 1995.Borri, M. and Merani, M.L., Performance and TCP-friendliness of the SQRT congestioncontrol protocols, IEEE Communication Letters, Vol. 8, No. 8, 541–543,August 2004.Chandrayana, K., Sikdar, B., and Kalyanaraman, S., Comparative study of TCPcompatible binomial congestion control schemes, Proceedings of the HighPerformance Switching and Routing, 2002, pp. 224–228.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. and Chang, C.-J., Design of a fuzzy traffic controller for ATM networks,IEEE/ACM Transactions on Networking, Vol. 4, No. 3, June 1996.Fall, K. and Floyd, S., Simulation-Based Comparisons of Tahoe, Reno and SACKTCP, Computer Communication Review, Vol. 26, No. 3, July 1996, pp. 5–21.Floyd, S. and Fall, K., Promoting the use of end-to-end congestion control in theInternet, IEEE/ACM Transactions on Networking, Vol. 7, No. 4, 458–472,August 1999.Floyd, S., A Report on Recent Developments in TCP Congestion Control, IEEECommunications Magazine, April 2001, pp. 84–90.Fuhrman, S., Kogan, Y., and Milito, R.A., An adaptive autonomous network congestioncontroller, Proceedings of the IEEE conference of Decision and Control,1996, pp. 301–306.Izmailov, R., Adaptive feedback control algorithms for large data transfers in highspeednetworks, IEEE Transactions on Automatic Control, Vol. 40, No. 8,1469–1471, August 1995.Jagannathan, S. and Talluri, J., Adaptive predictive congestion control of high-speednetworks, IEEE Transaction on Broadcasting, Vol. 48, No. 2, 129–139, June 2002.Jagannathan, S. and Talluri, J., Predictive congestion control of ATM networks:multiple sources/single buffer scenario, Proceedings of IEEE Conference onDecision and Control, Vol. 1, December 2000, pp. 47–52.Jagannathan, S. and Tohmaz, A., Congestion control of ATM networks using alearning methodology, Proceedings of the IEEE Conference on Control Applications,September 2001, pp. 135–140.Jagannathan, S., End to end congestion control of packet switched networks,Proceedings of the IEEE Conference on Control Applications, September 2002,pp. 519–524.Jagannathan, S., Neural Network Control of Nonlinear Discrete-Time Systems, Taylorand Francis, 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.Johari, R. and Tan, D.K.H, End-to-end congestion control for the Internet: delaysand stability, IEEE/ACM Transactions on Networking, Vol. 9, No. 6, 818–832,December 2001.
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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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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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Optimized Energy and Delay-Based Ro
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9Predictive Congestion Control for
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Predictive Congestion Control for W
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10Adaptive and Probabilistic Power
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Adaptive and Probabilistic Power Co
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