Vehicular task scheduling strategy with resource matching computing in cloud‐edge collaboration
Abstract In future transportation, on board unit (OBU) is a key component of connected vehicles with limited computing resources, and may not tackle the heavy computing burden from V2X networks. For these cases, we herein employ multi‐access edge cloud (MEC) and remote cloud to schedule the OBUs...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-12-01
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| Series: | IET Collaborative Intelligent Manufacturing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cim2.12023 |
| _version_ | 1811252127620661248 |
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| author | Fangyi Hu Lingling Lv TongLiang Zhang Yanjun Shi |
| author_facet | Fangyi Hu Lingling Lv TongLiang Zhang Yanjun Shi |
| author_sort | Fangyi Hu |
| collection | DOAJ |
| description | Abstract In future transportation, on board unit (OBU) is a key component of connected vehicles with limited computing resources, and may not tackle the heavy computing burden from V2X networks. For these cases, we herein employ multi‐access edge cloud (MEC) and remote cloud to schedule the OBUs' tasks. This schedule tries to minimise the total completion time of all tasks and the number of computing units of the MEC server. We first introduce a multi‐objective optimisation model considering the tasks and cloud‐edge collaboration. Then, we propose a task scheduling strategy considering the resource matching degree for this model. In this strategy, we propose an improved hybrid genetic algorithm and employ the resource matching measure between the tasks and computing units in terms of computing, storage and network bandwidth resources to obtain better solutions for generations. The numerical results showed the effectiveness of our strategy. |
| first_indexed | 2024-04-12T16:30:14Z |
| format | Article |
| id | doaj.art-8f5619c503804bcba0a9c8cdd283b624 |
| institution | Directory Open Access Journal |
| issn | 2516-8398 |
| language | English |
| last_indexed | 2024-04-12T16:30:14Z |
| publishDate | 2021-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Collaborative Intelligent Manufacturing |
| spelling | doaj.art-8f5619c503804bcba0a9c8cdd283b6242022-12-22T03:25:10ZengWileyIET Collaborative Intelligent Manufacturing2516-83982021-12-013433434410.1049/cim2.12023Vehicular task scheduling strategy with resource matching computing in cloud‐edge collaborationFangyi Hu0Lingling Lv1TongLiang Zhang2Yanjun Shi3Department of Mechanical Engineering Dalian University of Technology Dalian ChinaDepartment of Mechanical Engineering Dalian University of Technology Dalian ChinaDepartment of Mechanical Engineering Dalian University of Technology Dalian ChinaDepartment of Mechanical Engineering Dalian University of Technology Dalian ChinaAbstract In future transportation, on board unit (OBU) is a key component of connected vehicles with limited computing resources, and may not tackle the heavy computing burden from V2X networks. For these cases, we herein employ multi‐access edge cloud (MEC) and remote cloud to schedule the OBUs' tasks. This schedule tries to minimise the total completion time of all tasks and the number of computing units of the MEC server. We first introduce a multi‐objective optimisation model considering the tasks and cloud‐edge collaboration. Then, we propose a task scheduling strategy considering the resource matching degree for this model. In this strategy, we propose an improved hybrid genetic algorithm and employ the resource matching measure between the tasks and computing units in terms of computing, storage and network bandwidth resources to obtain better solutions for generations. The numerical results showed the effectiveness of our strategy.https://doi.org/10.1049/cim2.12023cloud computinggenetic algorithmsminimisationschedulingvehicular ad hoc networkstraffic engineering computing |
| spellingShingle | Fangyi Hu Lingling Lv TongLiang Zhang Yanjun Shi Vehicular task scheduling strategy with resource matching computing in cloud‐edge collaboration IET Collaborative Intelligent Manufacturing cloud computing genetic algorithms minimisation scheduling vehicular ad hoc networks traffic engineering computing |
| title | Vehicular task scheduling strategy with resource matching computing in cloud‐edge collaboration |
| title_full | Vehicular task scheduling strategy with resource matching computing in cloud‐edge collaboration |
| title_fullStr | Vehicular task scheduling strategy with resource matching computing in cloud‐edge collaboration |
| title_full_unstemmed | Vehicular task scheduling strategy with resource matching computing in cloud‐edge collaboration |
| title_short | Vehicular task scheduling strategy with resource matching computing in cloud‐edge collaboration |
| title_sort | vehicular task scheduling strategy with resource matching computing in cloud edge collaboration |
| topic | cloud computing genetic algorithms minimisation scheduling vehicular ad hoc networks traffic engineering computing |
| url | https://doi.org/10.1049/cim2.12023 |
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