期刊
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
卷 7, 期 4, 页码 2155-2164出版社
IEEE COMPUTER SOC
DOI: 10.1109/TNSE.2020.2991106
关键词
Bandwidth; Heuristic algorithms; Protocols; Reinforcement learning; Mathematical model; Resource management; Fuzzy systems; Congestion control; deep reinforcement learning; multi-path transport control protocol; software-defined networking
Multipath Transmission Control Protocol (MPTCP) enables multi-homed devices to establish multiple simultaneous routes for data transmission. Congestion Control (CC) is a fundamental mechanism for implementing and designing MPTCP. The Internet of Things (IoT) networks generate a massive volume of heterogeneous traffic with high dimensional states and diverse QoS characteristics. The existing MPTCP CC algorithms are unable to perform efficiently under highly mobile and dynamic IoT environments. We propose a novel model-free SDN-based adaptive actor-critic deep reinforcement learning framework based on a fuzzy normalized neural network to address the issue of CC for MPTCP in the IoT networks. In the proposed method, an agent can learn efficiently and better approximate the state-action value function of the actor and the action function of the critic to adjust the sub-flows congestion windows size adaptively according to the dynamic condition of a network. Simulation results show that the proposed scheme outperforms the state of the art schemes in terms of the goodput under highly-dynamic IoT environments.
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