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アイテム
Flow-Level Dynamic Bandwidth Allocation in SDN-Enabled Edge Cloud using Heuristic Reinforcement Learning
http://hdl.handle.net/10228/00008663
http://hdl.handle.net/10228/00008663460d156f-0f7f-4950-8107-1b85d919923e
| 名前 / ファイル | ライセンス | アクション |
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RECN_2021-08.pdf (579.0 kB)
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| アイテムタイプ | 学術雑誌論文 = Journal Article(1) | |||||||||||||
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| 公開日 | 2021-12-21 | |||||||||||||
| 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 | |||||||||||||
| タイトル | ||||||||||||||
| タイトル | Flow-Level Dynamic Bandwidth Allocation in SDN-Enabled Edge Cloud using Heuristic Reinforcement Learning | |||||||||||||
| 言語 | en | |||||||||||||
| 言語 | ||||||||||||||
| 言語 | eng | |||||||||||||
| 著者 |
Qadeer, Arslan
× Qadeer, Arslan× Lee, Myung J.× 塚本, 和也
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| 抄録 | ||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||
| 内容記述 | Edge Cloud (EC) is poised to brace massive machine type communication (mMTC) for 5G and IoT by providing compute and network resources at the edge. Yet, the EC being regionally domestic with a smaller scale, faces the challenges of bandwidth and computational throughput. Resource management techniques are considered necessary to achieve efficient resource allocation objectives. Software Defined Network (SDN) enabled EC architecture is emerging as a potential solution that enables dynamic bandwidth allocation and task scheduling for latency sensitive and diverse mobile applications in the EC environment. This study proposes a novel Heuristic Reinforcement Learning (HRL) based flow-level dynamic bandwidth allocation framework and validates it through end-to-end implementation using OpenFlow meter feature. OpenFlow meter provides granular control and allows demand-based flow management to meet the diverse QoS requirements germane to IoT traffics. The proposed framework is then evaluated by emulating an EC scenario based on real NSF COSMOS testbed topology at The City College of New York. A specific heuristic reinforcement learning with linear-annealing technique and a pruning principle are proposed and compared with the baseline approach. Our proposed strategy performs consistently in both Mininet and hardware OpenFlow switches based environments. The performance evaluation considers key metrics associated with real-time applications: throughput, end-to-end delay, packet loss rate, and overall system cost for bandwidth allocation. Furthermore, our proposed linear annealing method achieves faster convergence rate and better reward in terms of system cost, and the proposed pruning principle remarkably reduces control traffic in the network. | |||||||||||||
| 言語 | en | |||||||||||||
| 備考 | ||||||||||||||
| 内容記述タイプ | Other | |||||||||||||
| 内容記述 | 8th International Conference on Future Internet of Things and Cloud (FiCloud 2021), 23-25 August, 2021, Virtual (Online) | |||||||||||||
| 書誌情報 |
en : 2021 8th International Conference on Future Internet of Things and Cloud (FiCloud) p. 1-10, 発行日 2021-11-15 |
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| 出版社 | ||||||||||||||
| 出版者 | IEEE | |||||||||||||
| DOI | ||||||||||||||
| 関連タイプ | isVersionOf | |||||||||||||
| 識別子タイプ | DOI | |||||||||||||
| 関連識別子 | https://doi.org/10.1109/FiCloud49777.2021.00009 | |||||||||||||
| ISBN | ||||||||||||||
| 識別子タイプ | ISBN | |||||||||||||
| 関連識別子 | 978-1-6654-2574-2 | |||||||||||||
| 日本十進分類法 | ||||||||||||||
| 主題Scheme | NDC | |||||||||||||
| 主題 | 547 | |||||||||||||
| 著作権関連情報 | ||||||||||||||
| 権利情報 | Copyright (c) 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | Edge Cloud | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | Software Defined Networking | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | Reinforcement Learning | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | Bandwidth Allocation | |||||||||||||
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| 主題Scheme | Other | |||||||||||||
| 主題 | Resource Management | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | OpenFlow | |||||||||||||
| 出版タイプ | ||||||||||||||
| 出版タイプ | AM | |||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa | |||||||||||||
| 査読の有無 | ||||||||||||||
| 値 | yes | |||||||||||||
| 連携ID | ||||||||||||||
| 値 | 9774 | |||||||||||||
