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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
이 편지에서는 심층 강화 학습 행위자-비평 프레임워크를 사용하여 MPTCP 스케줄링 문제를 해결하기 위해 경로 역학 평가 비동기 이점 행위자-비평가 스케줄링 알고리즘(PDAA3C)을 제안합니다. 알고리즘은 멀티 코어 비동기 업데이트를 통해 최적의 전송 경로를 더 빠르게 선택하고 네트워크 공정성을 보장합니다. 기존 알고리즘과 비교하여 제안하는 알고리즘은 RLDS 알고리즘에 비해 8.6%의 처리량 이득을 달성하며 NS3 시뮬레이션의 이론적 상한에 접근합니다.
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
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
부
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
부
@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},}
부
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 -