The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Nesta carta, um algoritmo de escalonamento ator-crítico de vantagem assíncrona de avaliação de dinâmica de caminho (PDAA3C) é proposto para resolver o problema de escalonamento MPTCP usando a estrutura Ator-Crítico de aprendizagem por reforço profundo. O algoritmo escolhe o caminho de transmissão ideal mais rapidamente por meio da atualização assíncrona de vários núcleos e também garante a imparcialidade da rede. Comparado com os algoritmos existentes, o algoritmo proposto atinge 8.6% de ganho de rendimento em relação ao algoritmo RLDS e se aproxima do limite superior teórico na simulação NS3.
Teng LIANG
Zhejiang Sci-Tech University
Ao ZHAN
Zhejiang Sci-Tech University
Chengyu WU
Zhejiang Sci-Tech University
Zhengqiang WANG
Chongqing University of Posts and Telecommunication
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Teng LIANG, Ao ZHAN, Chengyu WU, Zhengqiang WANG, "PDAA3C: An A3C-Based Multi-Path Data Scheduling Algorithm" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 12, pp. 2127-2130, December 2022, doi: 10.1587/transinf.2022EDL8052.
Abstract: In this letter, a path dynamics assessment asynchronous advantage actor-critic scheduling algorithm (PDAA3C) is proposed to solve the MPTCP scheduling problem by using deep reinforcement learning Actor-Critic framework. The algorithm picks out the optimal transmitting path faster by multi-core asynchronous updating and also guarantee the network fairness. Compared with the existing algorithms, the proposed algorithm achieves 8.6% throughput gain over RLDS algorithm, and approaches the theoretic upper bound in the NS3 simulation.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022EDL8052/_p
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@ARTICLE{e105-d_12_2127,
author={Teng LIANG, Ao ZHAN, Chengyu WU, Zhengqiang WANG, },
journal={IEICE TRANSACTIONS on Information},
title={PDAA3C: An A3C-Based Multi-Path Data Scheduling Algorithm},
year={2022},
volume={E105-D},
number={12},
pages={2127-2130},
abstract={In this letter, a path dynamics assessment asynchronous advantage actor-critic scheduling algorithm (PDAA3C) is proposed to solve the MPTCP scheduling problem by using deep reinforcement learning Actor-Critic framework. The algorithm picks out the optimal transmitting path faster by multi-core asynchronous updating and also guarantee the network fairness. Compared with the existing algorithms, the proposed algorithm achieves 8.6% throughput gain over RLDS algorithm, and approaches the theoretic upper bound in the NS3 simulation.},
keywords={},
doi={10.1587/transinf.2022EDL8052},
ISSN={1745-1361},
month={December},}
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TY - JOUR
TI - PDAA3C: An A3C-Based Multi-Path Data Scheduling Algorithm
T2 - IEICE TRANSACTIONS on Information
SP - 2127
EP - 2130
AU - Teng LIANG
AU - Ao ZHAN
AU - Chengyu WU
AU - Zhengqiang WANG
PY - 2022
DO - 10.1587/transinf.2022EDL8052
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2022
AB - In this letter, a path dynamics assessment asynchronous advantage actor-critic scheduling algorithm (PDAA3C) is proposed to solve the MPTCP scheduling problem by using deep reinforcement learning Actor-Critic framework. The algorithm picks out the optimal transmitting path faster by multi-core asynchronous updating and also guarantee the network fairness. Compared with the existing algorithms, the proposed algorithm achieves 8.6% throughput gain over RLDS algorithm, and approaches the theoretic upper bound in the NS3 simulation.
ER -