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

Shiva Raj Pokhrel (), Jonathan Kua, Deol Satish, Sebnem Ozer, Jeff Howe and Anwar Walid
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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

Future Internet, 2024, vol. 16, issue 2, 1-15

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.

Keywords: multipath TCP; congestion control; experience-driven approach; performance-oriented congestion control; deep learning (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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