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| Paper Abstract and Keywords | ||
| Presentation | 2019-09-06 10:25 A Study on Fairness of Reinforcement Learning Based Congestion Control Meguru Yamazaki, Miki Yamamoto (Kansai Univ.)  NS2019-91 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | With fast deployment of high speed wireless access networks, communication environments for Internet accesses have been changing drastically. According to these wide range of network environments, many kinds of TCP congestion control algorithms have been proposed. Each of these TCP versions focuses on specific environment, e.g. wireless loss, and is designed with hard-wired logic, which means there is no universally applicable TCP algorithm. To resolve this technical problem related to hard-wired logic, several machine learning approaches for TCP congestion control has been proposed, e.g. QTCP. In this paper, we show that QTCP has technical problem of unfair condition due to a selfish behavior of machine learning approach. We propose a new QTCP algorithm which is based on AIMD(Additive Increase and Multiplicative Decrease). Our performance evaluation results show that our proposed improvement for QTCP shows good fairness behavior without degradation of throughput or queue length characteristics. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Congestion Control / Fairness / Reinforcemcent Learning / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 194, NS2019-91, pp. 13-18, Sept. 2019. | |
| Paper # | NS2019-91 | |
| Date of Issue | 2019-08-29 (NS) | |
| ISSN | Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | NS2019-91 |
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| Conference Information | |
| Committee | NS IN CS NV |
| Conference Date | 2019-09-05 - 2019-09-06 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Research Institute of Electrical Communication, Tohoku Univ. |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Session management (SIP/IMS), Interoperability/Standardization, NGN/NwGN/Future networks, Cloud/Data center networks, SDN (OpenFlow, etc.)/NFV, IPv6, Machine learning, etc. |
| Paper Information | |
| Registration To | NS |
| Conference Code | 2019-09-NS-IN-CS-NV |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Study on Fairness of Reinforcement Learning Based Congestion Control |
| Sub Title (in English) | |
| Keyword(1) | Congestion Control |
| Keyword(2) | Fairness |
| Keyword(3) | Reinforcemcent Learning |
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| 1st Author's Name | Meguru Yamazaki |
| 1st Author's Affiliation | Kansai University (Kansai Univ.) |
| 2nd Author's Name | Miki Yamamoto |
| 2nd Author's Affiliation | Kansai University (Kansai Univ.) |
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| Speaker | Author-1 |
| Date Time | 2019-09-06 10:25:00 |
| Presentation Time | 25 minutes |
| Registration for | NS |
| Paper # | NS2019-91 |
| Volume (vol) | vol.119 |
| Number (no) | no.194 |
| Page | pp.13-18 |
| #Pages | 6 |
| Date of Issue | 2019-08-29 (NS) |
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