Task-Driven Computing Offloading and Resource Allocation Scheme for Maritime Autonomous Surface Ships Under Cloud–Shore–Ship Collaboration Framework
Currently, Maritime Autonomous Surface Ships (MASS) have become one of the most attractive research areas in shipping and academic communities. Based on the ship-to-shore and ship-to-ship communication network, they can exploit diversified and distributed resources such as shore-based facilities and...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/1/16 |
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| author | Supu Xiu Ying Zhang Hualong Chen Yuanqiao Wen Changshi Xiao |
| author_facet | Supu Xiu Ying Zhang Hualong Chen Yuanqiao Wen Changshi Xiao |
| author_sort | Supu Xiu |
| collection | DOAJ |
| description | Currently, Maritime Autonomous Surface Ships (MASS) have become one of the most attractive research areas in shipping and academic communities. Based on the ship-to-shore and ship-to-ship communication network, they can exploit diversified and distributed resources such as shore-based facilities and cloud computing centers to execute a variety of ship applications. Due to the increasing number of MASS and asymmetrical distribution of traffic flows, the transportation management must design an efficient cloud–shore–ship collaboration framework and smart resource allocation strategy to improve the performance of the traffic network and provide high-quality applications to the ships. Therefore, we design a cloud–shore–ship collaboration framework, which integrates ship networking and cloud/edge computing and design the respective task collaboration process. It can effectively support the collaborative interaction of distributed resources in the cloud, onshore, and onboard. Based on the global information of the framework, we propose an intelligent resource allocation method based on Q-learning by combining the relevance, QoS characteristics, and priority of ship tasks. Simulation experiments show that our proposed approach can effectively reduce task latency and system energy consumption while supporting the concurrency of scale tasks. Compared with other analogy methods, the proposed algorithm can reduce the task processing delay by at least 15.7% and the task processing energy consumption by 15.4%. |
| format | Article |
| id | doaj-art-bb0c63d77aa0410b990c594e3f2736ac |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-bb0c63d77aa0410b990c594e3f2736ac2025-01-24T13:36:33ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-12-011311610.3390/jmse13010016Task-Driven Computing Offloading and Resource Allocation Scheme for Maritime Autonomous Surface Ships Under Cloud–Shore–Ship Collaboration FrameworkSupu Xiu0Ying Zhang1Hualong Chen2Yuanqiao Wen3Changshi Xiao4School of Electronic Information Engineering, Henan Institute of Technology, Xinxiang 453000, ChinaSchool of Electronic Information Engineering, Henan Institute of Technology, Xinxiang 453000, ChinaSchool of Navigation, Wuhan University of Technology, Wuhan 430070, ChinaState Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Navigation, Wuhan University of Technology, Wuhan 430070, ChinaCurrently, Maritime Autonomous Surface Ships (MASS) have become one of the most attractive research areas in shipping and academic communities. Based on the ship-to-shore and ship-to-ship communication network, they can exploit diversified and distributed resources such as shore-based facilities and cloud computing centers to execute a variety of ship applications. Due to the increasing number of MASS and asymmetrical distribution of traffic flows, the transportation management must design an efficient cloud–shore–ship collaboration framework and smart resource allocation strategy to improve the performance of the traffic network and provide high-quality applications to the ships. Therefore, we design a cloud–shore–ship collaboration framework, which integrates ship networking and cloud/edge computing and design the respective task collaboration process. It can effectively support the collaborative interaction of distributed resources in the cloud, onshore, and onboard. Based on the global information of the framework, we propose an intelligent resource allocation method based on Q-learning by combining the relevance, QoS characteristics, and priority of ship tasks. Simulation experiments show that our proposed approach can effectively reduce task latency and system energy consumption while supporting the concurrency of scale tasks. Compared with other analogy methods, the proposed algorithm can reduce the task processing delay by at least 15.7% and the task processing energy consumption by 15.4%.https://www.mdpi.com/2077-1312/13/1/16cloud–shore–ship collaborationMaritime Autonomous Surface Shipstask offloadingedge computingresource-allocation decision |
| spellingShingle | Supu Xiu Ying Zhang Hualong Chen Yuanqiao Wen Changshi Xiao Task-Driven Computing Offloading and Resource Allocation Scheme for Maritime Autonomous Surface Ships Under Cloud–Shore–Ship Collaboration Framework Journal of Marine Science and Engineering cloud–shore–ship collaboration Maritime Autonomous Surface Ships task offloading edge computing resource-allocation decision |
| title | Task-Driven Computing Offloading and Resource Allocation Scheme for Maritime Autonomous Surface Ships Under Cloud–Shore–Ship Collaboration Framework |
| title_full | Task-Driven Computing Offloading and Resource Allocation Scheme for Maritime Autonomous Surface Ships Under Cloud–Shore–Ship Collaboration Framework |
| title_fullStr | Task-Driven Computing Offloading and Resource Allocation Scheme for Maritime Autonomous Surface Ships Under Cloud–Shore–Ship Collaboration Framework |
| title_full_unstemmed | Task-Driven Computing Offloading and Resource Allocation Scheme for Maritime Autonomous Surface Ships Under Cloud–Shore–Ship Collaboration Framework |
| title_short | Task-Driven Computing Offloading and Resource Allocation Scheme for Maritime Autonomous Surface Ships Under Cloud–Shore–Ship Collaboration Framework |
| title_sort | task driven computing offloading and resource allocation scheme for maritime autonomous surface ships under cloud shore ship collaboration framework |
| topic | cloud–shore–ship collaboration Maritime Autonomous Surface Ships task offloading edge computing resource-allocation decision |
| url | https://www.mdpi.com/2077-1312/13/1/16 |
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