PDAA3C: An A3C-Based Multi-Path Data Scheduling Algorithm

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 async...

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Bibliographic Details
Published inIEICE Transactions on Information and Systems Vol. E105.D; no. 12; pp. 2127 - 2130
Main Authors WU, Chengyu, WANG, Zhengqiang, LIANG, Teng, ZHAN, Ao
Format Journal Article
LanguageEnglish
Published Tokyo The Institute of Electronics, Information and Communication Engineers 01.12.2022
Japan Science and Technology Agency
Subjects
Algorithms
data scheduler
Machine learning
MPTCP
network fairness
Scheduling
throughput
Upper bounds
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ISSN0916-8532
1745-1361
1745-1361
DOI10.1587/transinf.2022EDL8052

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Summary: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.
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ISSN:0916-8532
1745-1361
1745-1361
DOI:10.1587/transinf.2022EDL8052