A BP Neural Network Based Self-tuning for QoS Support in AVB Switched Ethernet

Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 237)

Abstract

To support QoS of time-sensitive services in Ethernet, IEEE has proposed a set of standards for transporting and forwarding real-time content over Ethernet known as Audio Video Bridging (AVB) with bandwidth reservation and priority isolation. AVB traffic is granted highest priority to ensure its transmission while low-priority traffic follows Strict Priority (SP). However, due to restrictions of SP algorithm, low-priority traffic may suffer a problem of starvation. To solve the problem, we propose a BP neural network based self-tuning controller (BPSC) over a probability selector to manage the transmission of best effort (BE) traffic in AVB switched Ethernet. This paper introduces the model of BPSC, followed by an simulation to demonstrate that BPSC could operate effectively and dynamically. The result shows that BPSC not only has the ability to manage the transmission precisely, but also shows both effectiveness and robustness.

Keywords

AVB BP neural networks Self-tuning Machine learning QoS 

Notes

Acknowledgments

This work was supported by the Natural Science Basic Research Plan in Shaanxi Province of China under Grant 2016JM6062, in part by the Aerospace Science and Technology Innovation Fund of China Aerospace Science and Technology Corporation, and in part by the Shanghai Aerospace Science and Technology Innovation Fund under Grant SAST2016034 and the China Fundamental Research Funds for the Central Universities under Grant No. 3102017ZY029.

References

  1. 1.
    Wang, Y., Shen, H., Duan, D.: On stabilization of quantized sampled-data neural-network-based control systems. IEEE Trans. Cybern. PP(99), 1–12 (2017)Google Scholar
  2. 2.
    Cao, J., Cuijpers, P.J.L., Bril, R.J., Lukkien, J.J.: Tight worst-case response-time analysis for ethernet AVB using eligible intervals. In: 12th IEEE World Conference on Factory Communication Systems, WFCS 2016, 3–6 May 2016, Aveiro, Portugal (2016)Google Scholar
  3. 3.
    Park, J.-D., Cheoun, B.-M., Jeon, J.-W.: Worst-case analysis of ethernet AVB in automotive system. In: 2015 IEEE International Conference on Information and Automation, ICIA 2015 - In Conjunction with 2015 IEEE International Conference on Automation and Logistics, 8–10 August 2015, Yunnan, China (2015)Google Scholar
  4. 4.
    Diemer, J., Rox, J., Ernst, R., Chen, F., Kremer, K.-T., Richter, K.: Exploring the worst-case timing of ethernet AVB for industrial applications. In: 38th Annual Conference on IEEE Industrial Electronics Society, IECON 2012, 25–28 October 2012, Montreal, QC, Canada (2012)Google Scholar
  5. 5.
    Liang, J., Qu, Y.: Intelligent Control Technology. Harbin Institute of Technology Press, Harbin (2016)Google Scholar
  6. 6.
    Funahashi, K.: On the approximate realization of continuous mappings by neural networks. Neural Netw. 2(3), 183–192 (1989)CrossRefGoogle Scholar
  7. 7.
    Gao, A., Hu, Y., Li, L., Li, X.: A feedback based probability selection for frame forwarding in AVB switched ethernet. In: 4th International Conference on Information Systems and Computing Technology (2016)Google Scholar
  8. 8.
    IEEE Standard for Local and Metropolitan Area Networks-Timing and Synchronization for Time- Sensitive Applications in Bridged Local Area Networks, IEEE Std 802.1As, November 2010Google Scholar
  9. 9.
    IEEE Standard for Local and Metropolitan Area Networks, Virtual Bridged Local Area Networks, Amendment 14, IEEE Std 802.1Qat, September 2010Google Scholar
  10. 10.
    IEEE Standard for Local and Metropolitan Area Networks, Virtual Bridged Local AreaNetworks, Amendment 12: Forwarding and Queuing Enhancements for Time-Sensitive Streams, IEEE Std 802.1Qav, January 2010Google Scholar
  11. 11.
    Diemer, J., Rox, J., Ernst, R.: Modeling of ethernet AVB networks for worst-case timing analysis. Math. Model. 45(1), 848–853 (2012)Google Scholar
  12. 12.
    Zhang, X.Y., Gao, P.J., Liu, Y.: The researching and simulation of BP neural network PID controller in industrys control system. Tech. Autom. Appl. 37, 9–12 (2010)Google Scholar
  13. 13.
    Diemer, J., Thiele, D., Ernst, R.: Formal worst-case timing analysis of ethernet topologies with strict-priority and AVB switching. In: International Symposium on Industrial Embedded Systems (2012)Google Scholar
  14. 14.
    Lim, H.T., Herrscher, D., Chaari, F.: Performance comparison of IEEE 802.1Q and IEEE 802.1 AVB in an ethernet-based in-vehicle network. In: International Conference on Computing Technology and Information Management (2012)Google Scholar
  15. 15.
    Alhowaide, A.A.Z., Doulat, A.S., Khamayseh, Y.M.: Performance evaluation of different scheduling algorithms in WiMAX. Int. J. Comput. Sci. Eng. Appl. 1(5), 81 (2011)Google Scholar
  16. 16.
    Lee, H., Lee, J., Park, C., Park, S.: Time-aware preemption to enhance the performance of audio/video bridging (AVB) in IEEE 802.1 TSN. In: IEEE International Conference on Computer Communication and the Internet. IEEE (2016)Google Scholar
  17. 17.
    Wang, T., Gao, H., Qiu, J.: A combined adaptive neural network and nonlinear model predictive control for multirate networked industrial process control. IEEE Trans. Neural Netw. Learn. Syst. 27(2), 416–425 (2016)MathSciNetCrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  1. 1.Northwestern Polytechnical UniversityXi’anChina

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