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Bandwidth Prediction and Congestion Control for ABR Traffic Based on Neural Networks

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3973))

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Abstract

In this paper, queue length in the buffer is considered as a scale to evaluate the quality of service of communication network. To realize that the system is stable and avoid the data congestion under complex environments, the neural networks predictor and controller are designed, which can predict the bursty available bandwidth for ABR traffic effectively and force the queue level in the buffer to the desired region, respectively. The fairness of different connections is achieved through fair algorithm. The simulations verify the effectiveness of the proposed scheme.

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References

  1. ATM From Management Specification 4.1, AF-TM-0121.0000 (1999)

    Google Scholar 

  2. Jain, R., Kalyanaraman, S., Viswanathan, R., Goyal, R.: A Sample Switch Algorithm. ATM Forum/95-0178 (1995)

    Google Scholar 

  3. Kalyanaraman, J.R., Fahmy, S.: The ERICA Switch Algorithm for ABR Traffic Manage-ment in ATM Networks. IEEE/ACM Trans. on Net. 8(1), 87–98 (2000)

    Article  Google Scholar 

  4. Tan, L.S., Yin, M.: A Rate-Based PI Congestion Controller for High-Speed Computer Communication Networks. Acta Electron. Sinica 30(8), 1138–1141 (2002)

    Google Scholar 

  5. Mascolo, S.: Congestion Control in High-Speed Communication Networks Using the Smith’s Principle. Automatica 35(12), 1921–1935 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  6. Laberteaux, K.P., Charles, E.R., Panos, J.A.: A Practical Controller for Explicit Rate Congestion Control. IEEE Trans. on Auto. Contr. 47(6), 960–977 (2002)

    Article  Google Scholar 

  7. Fei, X., He, X.: Fuzzy Neural Network Based Traffic Prediction and Congestion Control in High-Speed Networks. J. Comput. Sci & Tech. 15(3), 144–149 (2000)

    Article  MATH  Google Scholar 

  8. Soon, N.H., Sundararajan, N., Saratchandran, P.: ABR Traffic Management Using Minimal Resource Allocation (Neural) Networks. Comp. Com. 25(1), 9–20 (2002)

    Article  Google Scholar 

  9. Shu, Y.T., Wang, L., Zhang, L.F., et al.: Internet Traffic Modeling and Prediction Using FARIMA Models. Chin. J. of Comp. 24(1), 46–54 (2001)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Electrical Engineering, Yanshan Univesity, Qinhuangdao, 066004, Hebei, China

    Zhixin Liu, Xinping Guan & Huihua Wu

Authors
  1. Zhixin Liu
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  2. Xinping Guan
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  3. Huihua Wu
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd, 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Liu, Z., Guan, X., Wu, H. (2006). Bandwidth Prediction and Congestion Control for ABR Traffic Based on Neural Networks. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_30

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  • DOI: https://doi.org/10.1007/11760191_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34482-7

  • Online ISBN: 978-3-540-34483-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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