An intelligent scheme for congestion control: When active queue management meets deep reinforcement learning-Reference-Cited by-同舟云学术

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An intelligent scheme for congestion control: When active queue management meets deep reinforcement learning

Published:2021-12 Issue: Volume:200 Page:108515
ISSN:1389-1286
Container-title:Computer Networks
language:en
Short-container-title:Computer Networks

Author:

Ma Huihui^ORCID,Xu Du,Dai Yueyue,Dong Qing^ORCID

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Computer Networks and Communications

Reference23 articles.

1. AQM Characterization Guidelines;Kuhn,2015

2. Random early detection gateways for congestion avoidance;Floyd;IEEE/ACM Trans. Netw.,1993

3. On designing improved controllers for AQM routers supporting TCP flows;Hollot,2001

4. PIE: A lightweight control scheme to address the bufferbloat problem;Pan,2013

5. The BLUE active queue management algorithms;Feng;IEEE/ACM Trans. Netw.,2002

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1. An adaptive network congestion control strategy based on the change trend of average queue length;Computer Networks;2024-08

2. Active Queue Management in L4S with Asynchronous Advantage Actor-Critic: A FreeBSD Networking Stack Perspective;Future Internet;2024-07-25

3. Deep Reinforcement Learning Perspectives on Improving Reliable Transmissions in IoT Networks: Problem Formulation, Parameter Choices, Challenges, and Future Directions;Internet of Things;2023-10

4. Efficient congestion control in communications using novel weighted ensemble deep reinforcement learning;Computers and Electrical Engineering;2023-09

5. QueuePilot: Reviving Small Buffers With a Learned AQM Policy;IEEE INFOCOM 2023 - IEEE Conference on Computer Communications;2023-05-17

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