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A Novel Two-Layered Reinforcement Learning for Task Offloading with Tradeoff between Physical Machine Utilization Rate and Delay
https://tokushima-u.repo.nii.ac.jp/records/2010537
https://tokushima-u.repo.nii.ac.jp/records/20105375697004b-acfd-4d0b-866e-6d316fe6335e
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
futint_10_7_60.pdf (2.53 MB)
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| Item type | 文献 / Documents(1) | |||||||||||||||
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| 公開日 | 2022-12-07 | |||||||||||||||
| アクセス権 | ||||||||||||||||
| アクセス権 | open access | |||||||||||||||
| 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 | |||||||||||||||
| 出版社版DOI | ||||||||||||||||
| 識別子タイプ | DOI | |||||||||||||||
| 関連識別子 | https://doi.org/10.3390/fi10070060 | |||||||||||||||
| 言語 | ja | |||||||||||||||
| 関連名称 | 10.3390/fi10070060 | |||||||||||||||
| 出版タイプ | ||||||||||||||||
| 出版タイプ | VoR | |||||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||||||
| タイトル | ||||||||||||||||
| タイトル | A Novel Two-Layered Reinforcement Learning for Task Offloading with Tradeoff between Physical Machine Utilization Rate and Delay | |||||||||||||||
| 言語 | en | |||||||||||||||
| 著者 |
Quan, Li
× Quan, Li
× Wang, Zhiliang
× 任, 福継
WEKO
401
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| 抄録 | ||||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||||
| 内容記述 | Mobile devices could augment their ability via cloud resources in mobile cloud computing environments. This paper developed a novel two-layered reinforcement learning (TLRL) algorithm to consider task offloading for resource-constrained mobile devices. As opposed to existing literature, the utilization rate of the physical machine and the delay for offloaded tasks are taken into account simultaneously by introducing a weighted reward. The high dimensionality of the state space and action space might affect the speed of convergence. Therefore, a novel reinforcement learning algorithm with a two-layered structure is presented to address this problem. First, k clusters of the physical machines are generated based on the k-nearest neighbors algorithm (k-NN). The first layer of TLRL is implemented by a deep reinforcement learning to determine the cluster to be assigned for the offloaded tasks. On this basis, the second layer intends to further specify a physical machine for task execution. Finally, simulation examples are carried out to verify that the proposed TLRL algorithm is able to speed up the optimal policy learning and can deal with the tradeoff between physical machine utilization rate and delay. | |||||||||||||||
| 言語 | en | |||||||||||||||
| キーワード | ||||||||||||||||
| 言語 | en | |||||||||||||||
| 主題Scheme | Other | |||||||||||||||
| 主題 | mobile device | |||||||||||||||
| キーワード | ||||||||||||||||
| 言語 | en | |||||||||||||||
| 主題Scheme | Other | |||||||||||||||
| 主題 | task offloading | |||||||||||||||
| キーワード | ||||||||||||||||
| 言語 | en | |||||||||||||||
| 主題Scheme | Other | |||||||||||||||
| 主題 | tradeoff | |||||||||||||||
| キーワード | ||||||||||||||||
| 言語 | en | |||||||||||||||
| 主題Scheme | Other | |||||||||||||||
| 主題 | mobile cloud computing | |||||||||||||||
| キーワード | ||||||||||||||||
| 言語 | en | |||||||||||||||
| 主題Scheme | Other | |||||||||||||||
| 主題 | two layered reinforcement learning | |||||||||||||||
| 書誌情報 |
en : Future Internet 巻 10, 号 7, p. 60, 発行日 2018-07-01 |
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| 収録物ID | ||||||||||||||||
| 収録物識別子タイプ | ISSN | |||||||||||||||
| 収録物識別子 | 19995903 | |||||||||||||||
| 出版者 | ||||||||||||||||
| 出版者 | MDPI | |||||||||||||||
| 言語 | en | |||||||||||||||
| 権利情報 | ||||||||||||||||
| 言語 | en | |||||||||||||||
| 権利情報 | This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | |||||||||||||||
| EID | ||||||||||||||||
| 識別子 | 350475 | |||||||||||||||
| 識別子タイプ | URI | |||||||||||||||
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| 言語 | eng | |||||||||||||||
