Improving per-flow fairness by ML-based estimation of competing flows’ congestion control algorithm
Author:
Affiliation:
1. Tohoku University,Graduate School of Information Sciences,Sendai,Japan
2. Tohoku University,Research Institute of Electrical Communication,Sendai,Japan
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9829530/9829531/09829701.pdf?arnumber=9829701
Reference25 articles.
1. TCP-Drinc: Smart Congestion Control Based on Deep Reinforcement Learning
2. A Deep Reinforcement Learning Perspective on Internet Congestion Control;jay;Proceedings of ICML 2019,2019
3. Modest BBR: Enabling Better Fairness for BBR Congestion Control
4. Congestion Window Scaling Method for Inter-protocol Fairness of BBR
5. TCP-ArtaVegas: Improving the fairness of TCP-Vegas
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. On the Fairness of Internet Congestion Control over WiFi with Deep Reinforcement Learning;Future Internet;2024-09-10
2. Machine learning-based estimation of the number of competing flows at a bottleneck link;NOMS 2024-2024 IEEE Network Operations and Management Symposium;2024-05-06
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