期刊
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
卷 66, 期 12, 页码 10660-10675出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2017.2714704
关键词
Ant colony optimization; cloud computing; edge computing; job scheduling; vehicular network
资金
- China Scholarship Council [201206340036]
- JSPS Kakenhi [16H02817, 15K21599]
- Grants-in-Aid for Scientific Research [16H02817, 15K21599] Funding Source: KAKEN
With the emergence of in-vehicle applications, providing the required computational capabilities is becoming a crucial problem. This paperproposes a framework named autonomous vehicular edge (AVE) for edge computing on the road, with the aim of increasing the computational capabilities of vehicles in a decentralized manner. By managing the idle computational resources on vehicles and using them efficiently, the proposed AVE framework can provide computation services in dynamic vehicular environments without requiring particular infrastructures to be deployed. Specifically, this paper introduces a workflow to support the autonomous organization of vehicular edges. Efficient job caching is proposed to better schedule jobs based on the information collected on neighboring vehicles, including GPS information. A scheduling algorithm based on ant colony optimization is designed to solve this job assignment problem. Extensive simulations are conducted, and the simulation results demonstrate the superiority of this approach over competing schemes in typical urban and highway scenarios.
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