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Autonomic Communication pp 93–107Cite as

A Rate Feedback Predictive Control Scheme Based on Neural Network and Control Theory for Autonomic Communication

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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

  1. 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.

    Google Scholar 

  2. S. Keshav (1991) A control-theoretic approach to flow control, in: Proceedings of ACM SIGCOMM'91, Vol. 21, No. 4, pp.3–15.

    Google Scholar 

  3. D. Cavendish (1995) Proportional rate-based congestion control under long propagation delay, International Journal of Communication Systems, Vol. 8, pp. 79–89.

    Google Scholar 

  4. 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.

    Article  Google Scholar 

  5. 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.

    Article  Google Scholar 

  6. 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.

    Google Scholar 

  7. 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.

    Google Scholar 

  8. 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.

    Google Scholar 

  9. 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.

    Google Scholar 

  10. 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.

    Article  Google Scholar 

  11. J. Filipiak (1988) Modeling and Control of Dynamic Flows in Communication Networks, Springer Verlag Hardcover, New York.

    Google Scholar 

  12. 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.

    Google Scholar 

  13. 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.

    Google Scholar 

  14. 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.

    Article  Google Scholar 

  15. 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.

    Article  Google Scholar 

  16. Simon Haykin (1998) Neural Networks: A Comprehensive Foundation ,(2nd Edition), Prentice Hall, New York, July 6, 1998.

    Google Scholar 

  17. 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.

    Article  Google Scholar 

  18. 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.

    Google Scholar 

  19. S. Jagannathan (2001) Control of a class of nonlinear systems using multilayered neural networks, IEEE Transactions on Neural Networks, Vol.12, No. 5.

    Google Scholar 

  20. 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.

    Google Scholar 

  21. 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.

    Google Scholar 

  22. 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.

    Article  Google Scholar 

  23. Jeffrey O. Kephart, David M. Chess (2003) The Vision of Autonomic Computing, Computer, vol. 36, no. 1, pp. 41–50, January 2003.

    Google Scholar 

  24. 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.

    Article  Google Scholar 

  25. 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.

    Google Scholar 

  26. Athanasios V. Vasilakos, W. Pedrycz (2006) Ambient Intelligence, Wireless Networking and Ubiquitous Computing, ArtechHouse, MA, USA.

    Google Scholar 

  27. 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.

    Google Scholar 

  28. 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.

    Google Scholar 

  29. 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.

    Article  MathSciNet  Google Scholar 

  30. 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.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, Georgia State University, Atlanta, USA

    Naixue Xiong & Yingshu Li

  2. Department of Department of Electrical and Computer Engineering, University of Western Macedonia, Western Macedonia, Greece

    Athanasios V. Vasilakos

  3. Department of Computer Science, St. Francis Xavier University, Antigonish, NS, Canada

    Laurence T. Yang

  4. Department of Computer Science, Tsinghua University, Beijing, 100084, China

    Fei Long

  5. Digital Enterprise Research Institute, National University of Ireland, Galway, Galway, Ireland

    Lei Shu

Authors
  1. Naixue Xiong
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  2. Athanasios V. Vasilakos
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  3. Laurence T. Yang
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  4. Fei Long
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  5. Lei Shu
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  6. Yingshu Li
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Corresponding author

Correspondence to Naixue Xiong .

Editor information

Editors and Affiliations

  1. Dept. Computer &, University of Western Macedonia, Agios Dimitrios Park, Kozani, 501 00, Greece

    Athanasios V. Vasilakos

  2. Electrical & Computer Engineering Dept., Rutgers University, Brett Road 94, Piscataway, 08854-8058, U.S.A.

    Manish Parashar

  3. Corporate Research Centre, SAP AG, Vincent-Prießnitz-Str. 1, Karlsruhe, 76131, Germany

    Stamatis Karnouskos

  4. Dept. Electrical & Computer Engineering, University of Alberta, 116 Street 9107, Edmonton, T6G 2V4, Canada

    Witold Pedrycz

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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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  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-09752-7

  • Online ISBN: 978-0-387-09753-4

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