Achieving Flexible and Lightweight Multipath Congestion Control Through Online Learning-Reference-Cited by-同舟云学术

Achieving Flexible and Lightweight Multipath Congestion Control Through Online Learning

Published:2023-03 Issue:1 Volume:20 Page:46-59
ISSN:1932-4537
Container-title:IEEE Transactions on Network and Service Management
language:
Short-container-title:IEEE Trans. Netw. Serv. Manage.

Author:

Zhuang Rui¹^ORCID,Han Jiangping¹^ORCID,Xue Kaiping¹^ORCID,Li Jian¹^ORCID,Wei David S. L.²^ORCID,Li Ruidong³^ORCID,Sun Qibin¹,Lu Jun⁴

Affiliation:

1. School of Cyber Science and Technology, University of Science and Technology of China, Hefei, China

2. Department of Computer and Information Science, Fordham University, Bronx, NY, USA

3. College of Science and Engineering, Kanazawa University, Kanazawa, Japan

4. School of Cyber Science and Technology and the Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China

Funder

National Natural Science Foundation of China

Innovation Promotion Association of the Chinese Academy of Sciences

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications

Link

http://xplorestaging.ieee.org/ielx7/4275028/10065607/09899731.pdf?arnumber=9899731

Reference46 articles.

1. TCP ex machina

2. On the convergence of regret minimization dynamics in concave games

3. Two-Person Cooperative Games

4. A deep reinforcement learning perspective on Internet congestion control;jay;Proc 36th Int Conf Mach Learn (ICML),2019

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