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An Intelligent TCP Congestion Control Method Based on Deep Q Network

Author

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  • Yinfeng Wang

    (College of Software, Shenzhen Institute of Information Technology, Shenzhen 518116, China)

  • Longxiang Wang

    (College of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China)

  • Xiaoshe Dong

    (College of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China)

Abstract

To optimize the data migration performance between different supercomputing centers in China, we present TCP-DQN, which is an intelligent TCP congestion control method based on DQN (Deep Q network). The TCP congestion control process is abstracted as a partially observed Markov decision process. In this process, an agent is constructed to interact with the network environment. The agent adjusts the size of the congestion window by observing the characteristics of the network state. The network environment feeds back the reward to the agent, and the agent tries to maximize the expected reward in an episode. We designed a weighted reward function to balance the throughput and delay. Compared with traditional Q-learning, DQN uses double-layer neural networks and experience replay to reduce the oscillation problem that may occur in gradient descent. We implemented the TCP-DQN method and compared it with mainstream congestion control algorithms such as cubic, Highspeed and NewReno. The results show that the throughput of TCP-DQN can reach more than 2 times of the comparison method while the latency is close to the three compared methods.

Suggested Citation

  • Yinfeng Wang & Longxiang Wang & Xiaoshe Dong, 2021. "An Intelligent TCP Congestion Control Method Based on Deep Q Network," Future Internet, MDPI, vol. 13(10), pages 1-14, October.
    • Handle: RePEc:gam:jftint:v:13:y:2021:i:10:p:261-:d:652723
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    References listed on IDEAS

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    Cited by:

    1. Łukasz Piotr Łuczak & Przemysław Ignaciuk & Michał Morawski, 2023. "Evaluating MPTCP Congestion Control Algorithms: Implications for Streaming in Open Internet," Future Internet, MDPI, vol. 15(10), pages 1-17, October.

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