Abstract
The main difficulty arising in designing an efficient congestion control scheme lies in the large propagation delay in data transfer which usually leads to a mismatch between the network resources and the amount of admitted traffic. To attack this problem, this chapter describes a novel congestion control scheme that is based on a Back Propagation (BP) neural network technique.We consider a general computer communication model with multiple sources and one destination node. The dynamic buffer occupancy of the bottleneck node is predicted and controlled by using a BP neural network. The controlled best-effort traffic of the sources uses the bandwidth, which is left over by the guaranteed traffic. This control mechanism is shown to be able to avoid network congestion efficiently and to optimize the transfer performance both by the theoretic analyzing procedures and by the simulation studies.
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References
C. Q. Yang, A. A. S.Reddy (1995) A taxonomy for congestion control algorithms in packet switching networks. IEEE Network Magazine, Vol. 9, No.5, pp.34–45.
S. Keshav (1991) A control-theoretic approach to flow control, in: Proceedings of ACM SIGCOMM'91, Vol. 21, No. 4, pp.3–15.
D. Cavendish (1995) Proportional rate-based congestion control under long propagation delay, International Journal of Communication Systems, Vol. 8, pp. 79–89.
R. Jain, S. Kalyanaraman, S. Fahmy, R. Goyal (1996) Source behavior for ATM ABR traffic management: an explanation, IEEE Communication Magazine, Vol. 34, No. 11, pp. 50–57.
Rose Qingyang Hu and David W. Petr (2000) A Predictive Self-Tuning Fuzzy-Logic Feedback Rate Controller, IEEE/ACM Transactions on Networking, Vol. 8, No. 6, pp. 689–696.
Giuseppe Ascia, Vincenzo Catania, and Daniela Panno (2002) An efficient buffer management policy based on an integrated Fuzzy-GA approach, IEEE INFOCOM 2002, New York, No.107.
G. Ascia, V. Catania, G. Ficili and D. Panno (2001) A Fuzzy Buffer Management Scheme for ATM and IP Networks, IEEE INFOCOM 2001, Anchorage, Alaska, April 22–26, 2001, pp. 1539–1547.
J. Aweya, D.Y. Montuno, Qi-jun Zhang and L. Orozco-Barbosa (2000) Multi-step Neural Predictive Techniques for Congestion Control -Part 2: Control Procedures, International Journal of Parallel and Distributed Systems and Networks, Vol. 3, No. 3, pp. 139–143.
J. Aweya, D.Y. Montuno, Qi-jun Zhang and L. Orozco-Barbosa (2000) Multi-step Neural Predictive Techniques for Congestion Control -Part 1: Prediction and Control Models, International Journal of Parallel and Distributed Systems and Networks, Vol. 3, No. 1, pp. 1–8.
L. Benmohamed and S. M. Meerkov (1993) Feedback Control of Congestion in Packet Switching Networks: The Case of Single Congested Node, IEEE/ACM Transaction on Networking, Vol. 1, No. 6, pp. 693–708.
J. Filipiak (1988) Modeling and Control of Dynamic Flows in Communication Networks, Springer Verlag Hardcover, New York.
S. Jagannathan, and G. Galan (2003) A one-layer neural network controller with preprocessed inputs for autonomous underwater vehicles, IEEE Trans. on Vehicular Technology, Vo. 52, no. 5.
D. H. Wang, N. K. Lee and T. S. Dillon (2003) Extraction and Optimization of Fuzzy Protein Sequence Classification Rules Using GRBF Neural Networks, Neural Information Processing - Letters and Reviews, Vol.1. No. 1, pp. 53–59.
R. Yu and D. H. Wang (2003) Further study on structural properties of LTI singular systems under output feedback, Automatica, Vol.39, pp.685–692.
S. Jagannathan and J. Talluri (2002) Adaptive Predictive congestion control of High-Speed Networks, IEEE Transactions on Broadcasting, Vol.48, no.2, pp.129–139.
Simon Haykin (1998) Neural Networks: A Comprehensive Foundation ,(2nd Edition), Prentice Hall, New York, July 6, 1998.
F. Scarselli and A C Tsoi (1998) Universal Approximation Using FNN: A Survey of Some Existing Methods and Some New Results, Neural Networks, Vol. 11, pp. 15–37.
J. Alan Bivens, Boleslaw K. Szymanski, Mark J. Embrechts (2002) Network congestion arbitration and source problem prediction using neural networks, Smart Engineering System Design, vol. 4, N0. 243–252.
S. Jagannathan (2001) Control of a class of nonlinear systems using multilayered neural networks, IEEE Transactions on Neural Networks, Vol.12, No. 5.
P. Darbyshire and D.H. Wang (2003) Learning to Survive: Increased Learning Rates by Communication in a Multi-agent System, The 16th Australian Joint Conference on Artificial Intelligence (AI'03), Perth, Australia.
Lin, W. W. K., M. T. W. Ip, et al (2001) A Neural Network Based Proactive Buffer Control Approach for Better Reliability and Performance for Object-based Internet Applications, International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA 2001), Las Vegas, Nevada, USA, CSREA Press.
S. Dobson, S. Denazis, A. Fernĺćndez, D. Gaiti, E. Gelenbe, F. Massacci, P. Nixon, F. Saffre, N. Schmidt, F. Zambonelli (2006) A survey of autonomic communications, ACM Transactions on Autonomous and Adaptive Systems (TAAS), Vol. 1 , No. 2, pp. 223–259.
Jeffrey O. Kephart, David M. Chess (2003) The Vision of Autonomic Computing, Computer, vol. 36, no. 1, pp. 41–50, January 2003.
N. Laoutaris, O. Telelis, V. Zissimopoulos, I. Stavrakakis (2006) Distributed Selfish Replication, IEEE Transactions on Parallel and Distributed Systems, vol. 17, no. 12, pp. 1401–1413.
G. Acampora, M. Gaeta, V. Loia, and Athanasios V.Vasilakos (2009) Ubiquitous Findability of Fuzzy Services for Ambient Intelligence Applications, ACM Transactions on Autonomous and Adaptive Systems (TAAS), to appear.
Athanasios V. Vasilakos, W. Pedrycz (2006) Ambient Intelligence, Wireless Networking and Ubiquitous Computing, ArtechHouse, MA, USA.
N. Xiong, A. V. Vasilakos, L. T. Yang, L. Song, P. Yi, R. Kannan, and Y. Li. (2009) Comparative Analysis of Quality of Service and Memory Usage for Adaptive Failure Detectors in Healthcare Systems, IEEE Journal on Selected Areas in Communications (IEEE JSAC), to appear.
R. Quitadamo, F. Zambonelli (2008) Autonomic communication services: a new challenge for software agents, Autonomous Agents and Multi-Agent Systems, IEEE Transactions on automatic control, vol. 17, no. 3, pp. 457–475.
C. Park, D.J. Scheeres, V. Guibout, A. Bloch (2008) Global Solution for the Optimal Feedback Control of the Underactuated Heisenberg System, IEEE Transactions on automatic control, vol. 53, no. 11, pp. 2638–2642.
S. S. Ge, C. Yang, T. H. Lee (2008) Feedback-Linearization-Based Neural Adaptive Control for Unknown Nonaffine Nonlinear Discrete-Time Systems, IEEE Transactions on neural networks, vo. 19, no. 9, pp. 1599–1614.
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Xiong, N., Vasilakos, A.V., Yang, L.T., Long, F., Shu, L., Li, Y. (2009). A Rate Feedback Predictive Control Scheme Based on Neural Network and Control Theory for Autonomic Communication. In: Vasilakos, A., Parashar, M., Karnouskos, S., Pedrycz, W. (eds) Autonomic Communication. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09753-4_4
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DOI: https://doi.org/10.1007/978-0-387-09753-4_4
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