TCP-Drinc: Smart Congestion Control Based on Deep Reinforcement Learning

被引：44

作者：

Xiao, Kefan ^{[1
]}

Mao, Shiwen ^{[1
]}

Tugnait, Jitendra K. ^{[1
]}

机构：

[1] Auburn Univ, Dept Elect & Comp Engn, Auburn, AL 36849 USA

来源：

IEEE ACCESS | 2019年 / 7卷

关键词：

Congestion control; deep convolutional neural network (DCNN); deep reinforcement learning (DRL); long short term memory (LSTM); machine learning;

D O I：

10.1109/ACCESS.2019.2892046

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

As wired/wireless networks become more and more complex, the fundamental assumptions made by many existing TCP variants may not hold true anymore. In this paper, we develop a modelfree, smart congestion control algorithm based on deep reinforcement learning, which has a high potential in dealing with the complex and dynamic network environment. We present TCP-Deep ReInforcement learNing-based Congestion control (Drinc) which learns from past experience in the form of a set of measured features to decide how to adjust the congestion window size. We present the TCP-Drinc design and validate its performance with extensive ns-3 simulations and comparison with five benchmark schemes.

引用

页码：11892 / 11904

页数：13

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