A CBS Shaper Based on DDQN
Pages 1063 - 1067
Abstract
For the problem of using time-aware shaping combined with credit shaping-based hybrid traffic scheduling to achieve efficient transmission of hybrid streams, an improvement of CBS shaper based on deep reinforcement learning is proposed. By setting appropriate reward values, this mechanism greatly satisfies the different requirements of time delay for various types of audio and video streams and greatly reduces the delay and loss rate of BE streams without affecting the Time Triggered Flow (TT). The experimental results show that the algorithm improves the delay of AVB streams under the worst conditions. At the same time, the minimum delay and jitter are greatly improved compared with the classical model.
References
[1]
IEEE. IEEE standard for local and metropolitan area networks—bridges and bridged networks - amendment 25: enhancements for scheduled traffic: IEEE Std 802.1Qbv-2015 (amendment to IEEE Std 802.1Q-2014 as amended by IEEE Std 802.1Qca-2015, IEEE Std 802.1Qcd-2015, and IEEE Std 802.1Q-2014/Cor 1-2015)[S]. 2015.
[2]
IEEE. IEEE standard for local and metropolitan area networks–bridges and bridged networks-amendment 29: cyclic queuing and forwarding: IEEE 802.1Qch-2017 [S]. 2017.
[3]
IEEE. IEEE standard for local and metropolitan area networks—— virtual bridged local area networks-amendment 12: forwarding and queuing enhancements for time-sensitive streams: IEEE Std 802.1Qav-2009 (Amendment to IEEE Std 802.1Q-2005) [S]. 2009.
[4]
IEEE Standard for Local and metropolitan area networks–Audio Video Bridging(AVB)Systems[S]. IEEE Std 802. 1BA-2011
[5]
IEEE. IEEE standard for local and metropolitan area networks—Timing and synchronization for time-sensitiveapplicationsin bridged local area net- works:IEEE Std 802. 1AS-2011[S]. Piscataway, NJ:IEEE Press, 2002:1-48.
[6]
IEEE Std 802. 1Qat, IEEE Standard for Local and metropolitan area networks, Virtual Bridged Local Area Networks, Amendment 14:Stream Reser- vation Protocol(SRP)[S]. 2010.
[7]
Anschel, Oron, Nir Baram, and Nahum Shimkin. "Averaged-dqn: Variance reduction and stabilization for deep reinforcement learning." International conference on machine learning. PMLR, 2017.
[8]
IEEE Std. 802. 1Qav, IEEE Standard for Local and metropolitan area networks, Virtual Bridged Local Area Networks, Amendment 12:Forwarding and Queuing Enhancements for Time-Sensitive Streams[S], 2009.
[9]
Zhao, Luxi, "Timing analysis of AVB traffic in TSN networks using network calculus." 2018 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS). IEEE, 2018.
[10]
LI E S, HE F, XIONG H G. End-to-end traffic latency computation using frame shaping model in AVB network. Journal of Beijing University of Aeronautics and Astronsutics, 2017, 43(7): 1442-1449.
[11]
Van Hasselt H, Guez A, Silver D. Deep reinforcement learning with double q-learning[C]//Proceedings of the AAAI conference on artificial intelligence. 2016, 30(1).
[12]
Yijun MO, Zihan YANG, Huiyu LIU, Tianliu HE. Global cyclic queuing and forwarding mechanism for large-scale deterministic networks[J]. Telecommunications Science, 2021, 37(10): 55-65.
[13]
IEEE. IEEE standard for Ethernet: IEEE Std 802.3-2015 (Revision of IEEE Std 802.3-2012) [S]. 2016.
[14]
DECOTIGNIE J D. Ethernet-based real-time and industrial communi cations[J]. Proceedings of the IEEE, 2005, 93(6): 1102-1117.
[15]
Chen, Ye, "DQN-based power control for IoT transmission against jamming." 2018 IEEE 87th Vehicular Technology Conference (VTC Spring). IEEE, 2018.
[16]
HUANG T, WANG S, HUANG Y D, Survey of the deterministic network[J]. Journal on Communications, 2019, 40(6): 160-176.
Index Terms
- A CBS Shaper Based on DDQN
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Published In
September 2022
1221 pages
ISBN:9781450396899
DOI:10.1145/3573942
Copyright © 2022 ACM.
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Association for Computing Machinery
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Publication History
Published: 16 May 2023
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AIPR 2022
AIPR 2022: 2022 5th International Conference on Artificial Intelligence and Pattern Recognition
September 23 - 25, 2022
Xiamen, China
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