Behbehani, FS, Elgorashi, T and Elmirghani, JMH (2022) Optimized Processing Placement over a Vehicular Cloud. IEEE Access, 10. pp. 41411-41428. ISSN 2169-3536
Abstract
Modern vehicles equipped with on-board units (OBU) are playing an essential role in the emerging smart city revolution. The vehicular processing resources, however, are not used to their full potential. The concept of vehicular clouds is proposed to exploit the underutilized vehicular resources to supplement cloud computing services to relieve the burden on centralized cloud data centers and improve quality of service. In this paper we introduce a vehicular cloud architecture supported by fixed edge computing nodes and a central cloud data center. A mixed integer linear programming (MILP) model is developed to optimize the allocation of the processing demands in the distributed architecture while minimizing the overall power consumption. The results show power savings as high as 84% compared to processing in the conventional cloud. A heuristic algorithm with performance approaching that of the MILP model is developed to validate the MILP model and allocate processing demands in real time.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ |
Keywords: | vehicular clouds , edge computing , fog , power optimization , distributed processing , MILP |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/H040536/1 EPSRC (Engineering and Physical Sciences Research Council) EP/K016873/1 EPSRC (Engineering and Physical Sciences Research Council) EP/S016570/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 22 Apr 2022 10:13 |
Last Modified: | 25 Jun 2023 22:57 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ACCESS.2022.3167479 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:185763 |
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