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Fairness Improvement of Congestion Control with Reinforcement Learning
https://ipsj.ixsq.nii.ac.jp/records/212868
https://ipsj.ixsq.nii.ac.jp/records/212868ce0370ca-487d-4e23-888d-e4076af5e7bb
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
IPSJ-JNL6209019.pdf (749.6 kB)
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Copyright (c) 2021 by the Information Processing Society of Japan
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| オープンアクセス | ||
| Item type | Journal(1) | |||||||||
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| 公開日 | 2021-09-15 | |||||||||
| タイトル | ||||||||||
| タイトル | Fairness Improvement of Congestion Control with Reinforcement Learning | |||||||||
| タイトル | ||||||||||
| 言語 | en | |||||||||
| タイトル | Fairness Improvement of Congestion Control with Reinforcement Learning | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | [一般論文（テクニカルノート）] congestion control, fairness, reinforcement learning | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||
| 資源タイプ | journal article | |||||||||
| 著者所属 | ||||||||||
| Graduate School of Science and Engineering, Kansai University | ||||||||||
| 著者所属 | ||||||||||
| Graduate School of Science and Engineering, Kansai University | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Graduate School of Science and Engineering, Kansai University | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Graduate School of Science and Engineering, Kansai University | ||||||||||
| 著者名 |
Meguru, Yamazaki
× Meguru, Yamazaki
× Miki, Yamamoto
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| 著者名(英) |
Meguru, Yamazaki
× Meguru, Yamazaki
× Miki, Yamamoto
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| 論文抄録 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | With fast deployment of high speed wireless access networks, communication environments for internet access have been changing drastically. According to these wide range of network environments, a lot of TCP variants have been proposed. Each of these algorithms focuses on the specific environment and is designed with hardwired logic. This means there is no one-size-fits-all congestion control which can adapt to all environments. To resolve this problem, reinforcement learning based congestion control which learns operation suitable for each environment has been proposed. QTCP (Q-learning Based TCP) is one of the promising learning based TCPs. In this paper, we first reveal that a QTCP flow only behaves in the selfish manner of just increasing its own utility function, which causes unfairness between resource sharing flows. We propose a new QTCP congestion window control mechanism which is based on AIMD. Performance evaluation results show our proposal improves fairness without degrading high throughput and low latency characteristics of QTCP. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.29(2021) (online) DOI http://dx.doi.org/10.2197/ipsjjip.29.592 ------------------------------ |
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| 論文抄録(英) | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | With fast deployment of high speed wireless access networks, communication environments for internet access have been changing drastically. According to these wide range of network environments, a lot of TCP variants have been proposed. Each of these algorithms focuses on the specific environment and is designed with hardwired logic. This means there is no one-size-fits-all congestion control which can adapt to all environments. To resolve this problem, reinforcement learning based congestion control which learns operation suitable for each environment has been proposed. QTCP (Q-learning Based TCP) is one of the promising learning based TCPs. In this paper, we first reveal that a QTCP flow only behaves in the selfish manner of just increasing its own utility function, which causes unfairness between resource sharing flows. We propose a new QTCP congestion window control mechanism which is based on AIMD. Performance evaluation results show our proposal improves fairness without degrading high throughput and low latency characteristics of QTCP. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.29(2021) (online) DOI http://dx.doi.org/10.2197/ipsjjip.29.592 ------------------------------ |
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| 書誌レコードID | ||||||||||
| 収録物識別子タイプ | NCID | |||||||||
| 収録物識別子 | AN00116647 | |||||||||
| 書誌情報 |
情報処理学会論文誌 巻 62, 号 9, 発行日 2021-09-15 |
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| ISSN | ||||||||||
| 収録物識別子タイプ | ISSN | |||||||||
| 収録物識別子 | 1882-7764 | |||||||||
