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DDPG-MPCC: An Experience Driven Multipath Performance Oriented Congestion Control

Author

Listed:
  • Shiva Raj Pokhrel

    (IoT Research Lab, Deakin University, Geelong 3220, Australia)

  • Jonathan Kua

    (IoT Research Lab, Deakin University, Geelong 3220, Australia)

  • Deol Satish

    (IoT Research Lab, Deakin University, Geelong 3220, Australia)

  • Sebnem Ozer

    (Comcast Corporation, Philadelphia, PA 19103, USA)

  • Jeff Howe

    (Comcast Corporation, Philadelphia, PA 19103, USA)

  • Anwar Walid

    (Amazon Science, New York, NY 10001, USA)

Abstract

We introduce a novel multipath data transport approach at the transport layer referred to as ‘ Deep Deterministic Policy Gradient for Multipath Performance-oriented Congestion Control ’ (DDPG-MPCC), which leverages deep reinforcement learning to enhance congestion management in multipath networks. Our method combines DDPG with online convex optimization to optimize fairness and performance in simultaneously challenging multipath internet congestion control scenarios. Through experiments by developing kernel implementation, we show how DDPG-MPCC performs compared to the state-of-the-art solutions.

Suggested Citation

  • Shiva Raj Pokhrel & Jonathan Kua & Deol Satish & Sebnem Ozer & Jeff Howe & Anwar Walid, 2024. "DDPG-MPCC: An Experience Driven Multipath Performance Oriented Congestion Control," Future Internet, MDPI, vol. 16(2), pages 1-15, January.
    • Handle: RePEc:gam:jftint:v:16:y:2024:i:2:p:37-:d:1324904
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