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

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


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.


AVB BP neural networks Self-tuning Machine learning QoS 



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.


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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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