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

共 50 条

[41] Fuzzy-logic-based TCP congestion control system
Al-Naamany, AM
Bourdoucen, H
NETWORK CONTROL AND ENGINEERING FOR QOS, SECURITY AND MOBILITY II, 2003, 133 : 180 - 190
[42] TCP/AWM Network Congestion Control Based on Minimax Theory
Bai, Yun
Shen, Jindong
Jing, Yuanwei
PROCEEDINGS OF THE 33RD CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2021), 2021, : 6645 - 6650
[43] The TCP-Based New AIMD Congestion Control Algorithm
Jasem, Hayder Natiq
Zukarnain, Zuriati Ahmad
Othman, Mohamed
Subramaniam, Shamala
INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY, 2008, 8 (10): : 331 - 338
[44] Deep Reinforcement Learning for Smart City Communication Networks
Xia, Zhenchang
Xue, Shan
Wu, Jia
Chen, Yanjiao
Chen, Junjie
Wu, Libing
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2021, 17 (06) : 4188 - 4196
[45] Implementing Reinforcement Learning Datacenter Congestion Control in NVIDIA NICs
Fuhrer, Benjamin
Shpigelman, Yuval
Tessler, Chen
Mannor, Shie
Chechik, Gal
Zahavi, Eitan
Dalal, Gal
2023 IEEE/ACM 23RD INTERNATIONAL SYMPOSIUM ON CLUSTER, CLOUD AND INTERNET COMPUTING, CCGRID, 2023, : 331 - 343
[46] Partially Oblivious Congestion Control for the Internet via Reinforcement Learning
Sacco, Alessio
Flocco, Matteo
Esposito, Flavio
Marchetto, Guido
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT, 2023, 20 (02): : 1644 - 1659
[47] A Distributed Reinforcement Learning Approach to In-network Congestion Control
Mai, Tianle
Yao, Haipeng
Zhang, Xing
Xiong, Zehui
Niyato, Dusit
2020 IEEE/CIC INTERNATIONAL CONFERENCE ON COMMUNICATIONS IN CHINA (ICCC), 2020, : 817 - 822
[48] Deep Reinforcement Learning Based Volt-VAR Optimization in Smart Distribution Systems
Zhang, Ying
Wang, Xinan
Wang, Jianhui
Zhang, Yingchen
IEEE TRANSACTIONS ON SMART GRID, 2021, 12 (01) : 361 - 371
[49] End-to-End Loss Based TCP Congestion Control Mechanism as a Secured Communication Technology for Smart Healthcare Enterprises
Ahmad, Mudassar
Hussain, Majid
Abbas, Beenish
Aldabbas, Omar
Jamil, Uzma
Ashraf, Rehan
Asadi, Shahla
IEEE ACCESS, 2018, 6 : 11641 - 11656
[50] Improving the Congestion Control Performance for Mobile Networks in High-Speed Railway via Deep Reinforcement Learning
Cui, Laizhong
Yuan, Zuxian
Ming, Zhongxing
Yang, Shu
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2020, 69 (06) : 5864 - 5875

← 1 2 3 4 5 →