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A self-tuning controller for queuing delay regulation in TCP/AQM networks

Author

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  • Ghasem Kahe

    (Ministry of Science, Research, and Technology)

  • Amir Hossein Jahangir

    (Sharif University of Technology)

Abstract

AQM router aims primarily to control the network congestion through marking/dropping packets which are used as congestion feedback in traffic sources to balance their flow rate. However, stabilizing queuing delay and maximizing link utilization have been considered as the main control objectives, especially in media dominated networks. Usually, most of the AQM algorithms are designed for a nominal operating point. However, time-varying nature of network parameters frequently violates their robustness bounds. In this paper, a self-tuning compensated PID controller is proposed to address the time-varying nature of network conditions caused by parameter variations and unresponsive connections. The proposed scheme consists of network parameter estimation and a self-tuning AQM. Traffic load, network delay, and bottleneck link capacity are the time-varying network parameters whose variation effects should be compensated by the controller gains adaptation. As the controller gains are simply and directly obtained from the dynamic model, the obtained self-tuning controller can reasonably adapt itself to different operating conditions, while preserving the simplicity of the PI controllers. Packet-level simulations using ns2 show the outperformance of the developed controller for both latency regulation and resource utilization.

Suggested Citation

  • Ghasem Kahe & Amir Hossein Jahangir, 2019. "A self-tuning controller for queuing delay regulation in TCP/AQM networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 71(2), pages 215-229, June.
  • Handle: RePEc:spr:telsys:v:71:y:2019:i:2:d:10.1007_s11235-018-0526-1
    DOI: 10.1007/s11235-018-0526-1
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    References listed on IDEAS

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    1. Shalabh Bhatnagar & Sanjeev Patel & Karmeshu, 2018. "A stochastic approximation approach to active queue management," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 68(1), pages 89-104, May.
    2. Ping Wang & Daji Zhu & Xiaohui Lu, 2017. "Active queue management algorithm based on data-driven predictive control," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 64(1), pages 103-111, January.
    3. Lukasz Chrost & Andrzej Chydzinski, 2016. "On the deterministic approach to active queue management," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 63(1), pages 27-44, September.
    4. Ghulam Abbas & Sanaullah Manzoor & Masroor Hussain, 2018. "A stateless fairness-driven active queue management scheme for efficient and fair bandwidth allocation in congested Internet routers," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 67(1), pages 3-20, January.
    5. Lina He & Hairui Zhou, 2017. "Robust Lyapunov–Krasovskii based design for explicit control protocol against heterogeneous delays," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 66(3), pages 377-392, November.
    6. Karmeshu & Sanjeev Patel & Shalabh Bhatnagar, 2017. "Adaptive mean queue size and its rate of change: queue management with random dropping," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 65(2), pages 281-295, June.
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    Cited by:

    1. Wladimir Gonçalves Morais & Carlos Eduardo Maffini Santos & Carlos Marcelo Pedroso, 2022. "Application of active queue management for real-time adaptive video streaming," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 79(2), pages 261-270, February.
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    3. Saneh Lata Yadav & R. L. Ujjwal, 2021. "Mitigating congestion in wireless sensor networks through clustering and queue assistance: a survey," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2083-2098, December.

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