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

Future Internet Pub Date : 2024-01-23 DOI:10.3390/fi16020037
Shiva Raj Pokhrel, Jonathan Kua, Deol Satish, Sebnem Ozer, Jeff Howe, Anwar Walid
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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.
DDPG-MPCC:以经验为导向的多路径性能拥塞控制
我们在传输层引入了一种新颖的多路径数据传输方法,称为 "面向多路径性能的拥塞控制深度确定性策略梯度"(DDPG-MPCC),它利用深度强化学习来加强多路径网络的拥塞管理。我们的方法将 DDPG 与在线凸优化相结合,在同时具有挑战性的多径互联网拥塞控制场景中优化公平性和性能。通过开发内核实现的实验,我们展示了 DDPG-MPCC 与最先进解决方案相比的表现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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