Framework of Cloud Computing Resource Scheduling for Vehicle Fault Diagnosis
Internet of Vehicles (IoVs) provides communication and computing resources, which makes the on-board diagnosis of vehicle faults possible. However, those resources need to be expanded to support the accurate analysis of the on-board diagnosis. Vehicular Cloud Computing (VCC) can solve the pressure o...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10400422/ |
| _version_ | 1797243359297798144 |
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| author | Wanyi Gu Hua Xu Lina Zhu |
| author_facet | Wanyi Gu Hua Xu Lina Zhu |
| author_sort | Wanyi Gu |
| collection | DOAJ |
| description | Internet of Vehicles (IoVs) provides communication and computing resources, which makes the on-board diagnosis of vehicle faults possible. However, those resources need to be expanded to support the accurate analysis of the on-board diagnosis. Vehicular Cloud Computing (VCC) can solve the pressure of local vehicle processing but will cause an unavoidable delay. Thus, the accuracy and timeliness of on-board diagnosis cannot be guaranteed. To address the issue, we propose a Mobile Edge Caching based Resource Scheduling (MECRS) mechanism for the on-board diagnosis of vehicle faults. According to the urgency of vehicle fault diagnosis, we first design a cloud scheduling algorithm to meet the computation requirements of both the essential business of IoVs and the fault diagnosis. Subsequently, the priority allocation strategy is made for all four types of requests. Then, the urgent requests can be processed timely. Specifically, a multi-objective optimization method is proposed to allocate communication and computing resources for the above requests. In addition, we present a mobile edge caching algorithm in which the large-scale file with high popularity is offloaded to alleviate the pressure of the cloud. Finally, we carry out comprehensive simulations. The results reveal that the developed mechanism provides a high service rate for on-board diagnosis with limited network resources, while the performances of the other three essential services are not compromised. |
| first_indexed | 2024-04-24T18:53:52Z |
| format | Article |
| id | doaj.art-476feaa8965044418824e7a952abea22 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T18:53:52Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-476feaa8965044418824e7a952abea222024-03-26T17:45:57ZengIEEEIEEE Access2169-35362024-01-0112360963610910.1109/ACCESS.2024.335427210400422Framework of Cloud Computing Resource Scheduling for Vehicle Fault DiagnosisWanyi Gu0https://orcid.org/0000-0002-6430-1836Hua Xu1Lina Zhu2https://orcid.org/0000-0003-4486-9130Information and Navigation College, Air Force Engineering University, Xi’an, ChinaInformation and Navigation College, Air Force Engineering University, Xi’an, ChinaState Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, ChinaInternet of Vehicles (IoVs) provides communication and computing resources, which makes the on-board diagnosis of vehicle faults possible. However, those resources need to be expanded to support the accurate analysis of the on-board diagnosis. Vehicular Cloud Computing (VCC) can solve the pressure of local vehicle processing but will cause an unavoidable delay. Thus, the accuracy and timeliness of on-board diagnosis cannot be guaranteed. To address the issue, we propose a Mobile Edge Caching based Resource Scheduling (MECRS) mechanism for the on-board diagnosis of vehicle faults. According to the urgency of vehicle fault diagnosis, we first design a cloud scheduling algorithm to meet the computation requirements of both the essential business of IoVs and the fault diagnosis. Subsequently, the priority allocation strategy is made for all four types of requests. Then, the urgent requests can be processed timely. Specifically, a multi-objective optimization method is proposed to allocate communication and computing resources for the above requests. In addition, we present a mobile edge caching algorithm in which the large-scale file with high popularity is offloaded to alleviate the pressure of the cloud. Finally, we carry out comprehensive simulations. The results reveal that the developed mechanism provides a high service rate for on-board diagnosis with limited network resources, while the performances of the other three essential services are not compromised.https://ieeexplore.ieee.org/document/10400422/Mobile edge cachingvehicle fault diagnosisresource schedulingvehicular cloud computing |
| spellingShingle | Wanyi Gu Hua Xu Lina Zhu Framework of Cloud Computing Resource Scheduling for Vehicle Fault Diagnosis IEEE Access Mobile edge caching vehicle fault diagnosis resource scheduling vehicular cloud computing |
| title | Framework of Cloud Computing Resource Scheduling for Vehicle Fault Diagnosis |
| title_full | Framework of Cloud Computing Resource Scheduling for Vehicle Fault Diagnosis |
| title_fullStr | Framework of Cloud Computing Resource Scheduling for Vehicle Fault Diagnosis |
| title_full_unstemmed | Framework of Cloud Computing Resource Scheduling for Vehicle Fault Diagnosis |
| title_short | Framework of Cloud Computing Resource Scheduling for Vehicle Fault Diagnosis |
| title_sort | framework of cloud computing resource scheduling for vehicle fault diagnosis |
| topic | Mobile edge caching vehicle fault diagnosis resource scheduling vehicular cloud computing |
| url | https://ieeexplore.ieee.org/document/10400422/ |
| work_keys_str_mv | AT wanyigu frameworkofcloudcomputingresourceschedulingforvehiclefaultdiagnosis AT huaxu frameworkofcloudcomputingresourceschedulingforvehiclefaultdiagnosis AT linazhu frameworkofcloudcomputingresourceschedulingforvehiclefaultdiagnosis |