Joint Optimization of Resources in Fog-Radio Access Network with Binary Computation Offloading
With the dramatic increase in the number of emerging Internet services, the Fog-Radio Access Network (F-RAN) has recently emerged as a promising paradigm to enhance high-load task processing capabilities for mobile devices, such as the Internet of things (IoT) and mobile terminals. Hence, it becomes...
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| Veröffentlicht in: | Mobile information systems Jg. 2023; S. 1 - 15 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Amsterdam
Hindawi
21.04.2023
John Wiley & Sons, Inc |
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| ISSN: | 1574-017X, 1875-905X |
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| Abstract | With the dramatic increase in the number of emerging Internet services, the Fog-Radio Access Network (F-RAN) has recently emerged as a promising paradigm to enhance high-load task processing capabilities for mobile devices, such as the Internet of things (IoT) and mobile terminals. Hence, it becomes a challenge for the F-RAN to reduce the offloading cost by designing an effective offloading strategy and rational planning of limited network resources to improve the quality of experience (QoE). This article investigates the F-RAN with a binary offload policy. It proposes an intelligent algorithm capable of optimally adapting to task offload policy, fog computing resource allocation, and offload channel resource allocation. To evaluate the offloading strategy intuitively, we design a system utility metric defined as a delay-energy weighted sum. The joint optimization problem is converted into a convex problem based on this metric, i.e., a mixed integer nonlinear programming (MINLP) problem. A novel algorithm based on improved double-deep Q neural networks is DDQN, which is proposed to address this problem. Furthermore, an action space mapping method in the DDQN framework is presented to obtain offloading decisions. Extensive experimental data indicate that the proposed DDQN algorithm can effectively reduce the offloading cost and is adaptable to different offloading scenarios. |
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| AbstractList | With the dramatic increase in the number of emerging Internet services, the Fog-Radio Access Network (F-RAN) has recently emerged as a promising paradigm to enhance high-load task processing capabilities for mobile devices, such as the Internet of things (IoT) and mobile terminals. Hence, it becomes a challenge for the F-RAN to reduce the offloading cost by designing an effective offloading strategy and rational planning of limited network resources to improve the quality of experience (QoE). This article investigates the F-RAN with a binary offload policy. It proposes an intelligent algorithm capable of optimally adapting to task offload policy, fog computing resource allocation, and offload channel resource allocation. To evaluate the offloading strategy intuitively, we design a system utility metric defined as a delay-energy weighted sum. The joint optimization problem is converted into a convex problem based on this metric, i.e., a mixed integer nonlinear programming (MINLP) problem. A novel algorithm based on improved double-deep Q neural networks is DDQN, which is proposed to address this problem. Furthermore, an action space mapping method in the DDQN framework is presented to obtain offloading decisions. Extensive experimental data indicate that the proposed DDQN algorithm can effectively reduce the offloading cost and is adaptable to different offloading scenarios. |
| Author | Bai, Wenle Wang, Zhuoqi |
| Author_xml | – sequence: 1 givenname: Wenle surname: Bai fullname: Bai, Wenle organization: Information Science and TechnologyNorth China University of TechnologyShijingshan DistrictBeijing 100043Chinancut.edu.cn – sequence: 2 givenname: Zhuoqi orcidid: 0000-0002-6352-6355 surname: Wang fullname: Wang, Zhuoqi organization: School of Information Science and TechnologyNorth China University of TechnologyShijingshan DistrictBeijing 100043Chinancut.edu.cn |
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| CitedBy_id | crossref_primary_10_1007_s11227_024_06454_6 crossref_primary_10_1007_s11235_025_01332_9 |
| Cites_doi | 10.23919/jcc.2019.10.013 10.1109/access.2020.3022661 10.1109/twc.2017.2703901 10.1109/jiot.2020.2971323 10.1088/1742-6596/1873/1/012046 10.1109/access.2019.2929075 10.1109/access.2019.2941741 10.1109/ACCESS.2020.3000832 10.1109/jcn.2018.000036 10.1109/twc.2020.3007805 10.1109/access.2021.3058021 10.1109/access.2019.2947542 10.1109/JIOT.2022.3168885 10.1109/tmc.2019.2952354 10.1109/twc.2018.2821664 10.1109/TMC.2019.2892953 10.1109/tmc.2020.2967041 10.1109/ACCESS.2019.2947652 10.1109/tc.2020.2993561 10.1109/ICCC52777.2021.9580391 10.1049/iet-com.2017.0213 10.1109/TVT.2019.2919915 10.1109/tmc.2019.2928811 10.1109/jiot.2020.2972061 10.1109/PIMRC48278.2020.9217355 10.1109/tvt.2020.2981122 10.23919/icn.2020.0020 10.1109/access.2020.3004861 10.1109/JSAC.2020.3020659 10.1109/access.2021.3067702 10.1109/tvt.2018.2881191 10.1109/ICISPC.2019.8935698 10.1109/TWC.2020.2978843 10.1109/tcss.2020.3005761 10.1109/ACCESS.2020.3045304 10.1109/jiot.2020.3022699 |
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| Copyright | Copyright © 2023 Wenle Bai and Zhuoqi Wang. Copyright © 2023 Wenle Bai and Zhuoqi Wang. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 |
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| References | 24 25 26 M. Tang (40) 2020 27 S. Balasubramanian (23) 28 Q. Li (29) 2019; 16 30 Y. Ouyang (37) 2021 31 10 32 11 33 12 34 13 35 14 36 15 16 38 17 39 18 Z. Shuchen (22) 2020; 178 19 1 2 Y. Jie (21) 2019; 16 4 5 6 7 8 9 M. Muniswamaiah (3) 41 20 |
| References_xml | – ident: 8 doi: 10.23919/jcc.2019.10.013 – volume: 16 start-page: 22 issue: 3 year: 2019 ident: 21 article-title: Game-theoretic online resource allocation scheme on fog computing for mobile multimedia users publication-title: China Commun – volume: 178 start-page: 1389 issue: 3 year: 2020 ident: 22 article-title: The partial computation offloading strategy based on game theory for multi-user in mobile edge computing environment publication-title: Computer Networks – volume: 16 start-page: 32 issue: 3 year: 2019 ident: 29 article-title: Energy-efficient computation offloading and resource allocation in fog computing for internet of everything publication-title: China Commun – ident: 1 doi: 10.1109/access.2020.3022661 – start-page: 139 ident: 3 article-title: A Survey on Cloudlets, mobile Edge, and Fog Computing – ident: 10 doi: 10.1109/twc.2017.2703901 – ident: 16 doi: 10.1109/jiot.2020.2971323 – volume-title: Journal of Physics: Conference Series year: 2021 ident: 37 article-title: Task offloading algorithm of vehicle edge computing environment based on Dueling-DQN doi: 10.1088/1742-6596/1873/1/012046 – ident: 9 doi: 10.1109/access.2019.2929075 – ident: 15 doi: 10.1109/access.2019.2941741 – ident: 11 doi: 10.1109/ACCESS.2020.3000832 – ident: 24 doi: 10.1109/jcn.2018.000036 – ident: 28 doi: 10.1109/twc.2020.3007805 – ident: 41 doi: 10.1109/access.2021.3058021 – ident: 33 doi: 10.1109/access.2019.2947542 – ident: 6 doi: 10.1109/JIOT.2022.3168885 – ident: 31 doi: 10.1109/tmc.2019.2952354 – ident: 13 doi: 10.1109/twc.2018.2821664 – ident: 20 doi: 10.1109/TMC.2019.2892953 – start-page: 1 year: 2020 ident: 40 article-title: Deep reinforcement learning for task offloading in mobile edge computing systems publication-title: IEEE Transactions on Mobile Computing – ident: 7 doi: 10.1109/tmc.2020.2967041 – ident: 5 doi: 10.1109/ACCESS.2019.2947652 – ident: 32 doi: 10.1109/tc.2020.2993561 – ident: 36 doi: 10.1109/ICCC52777.2021.9580391 – ident: 17 doi: 10.1049/iet-com.2017.0213 – ident: 25 doi: 10.1109/TVT.2019.2919915 – ident: 38 doi: 10.1109/tmc.2019.2928811 – ident: 19 doi: 10.1109/jiot.2020.2972061 – ident: 35 doi: 10.1109/PIMRC48278.2020.9217355 – ident: 12 doi: 10.1109/tvt.2020.2981122 – ident: 34 doi: 10.23919/icn.2020.0020 – ident: 39 doi: 10.1109/access.2020.3004861 – ident: 26 doi: 10.1109/JSAC.2020.3020659 – ident: 18 doi: 10.1109/access.2021.3067702 – ident: 14 doi: 10.1109/tvt.2018.2881191 – ident: 23 article-title: Enhancing the computational intelligence of smart fog gateway with boundary-constrained dynamic time warping based imputation and data reduction doi: 10.1109/ICISPC.2019.8935698 – ident: 27 doi: 10.1109/TWC.2020.2978843 – ident: 30 doi: 10.1109/tcss.2020.3005761 – ident: 2 doi: 10.1109/ACCESS.2020.3045304 – ident: 4 doi: 10.1109/jiot.2020.3022699 |
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| SubjectTerms | Algorithms Communication Computation offloading Convex analysis Decision making Deep learning Edge computing Electronic devices Energy consumption Game theory Integer programming Internet of Things Mixed integer Neural networks Nonlinear programming Optimization Reputations Resource allocation Servers Strategy |
| Title | Joint Optimization of Resources in Fog-Radio Access Network with Binary Computation Offloading |
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