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Vehicular Task Offloading and Job Scheduling Method Based on Cloud-Edge Computing
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DOI：10.1109/TITS.2023.3300437.png)
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Abstract
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With the rapid development of intelligent transportation systems (ITS), the Internet of Vehicles (IoV) is gradually becoming mature, but at the same time, the scale of intelligent vehicles on the road will increase rapidly, and the traditional cloud computing architecture cannot meet the requirements of low delay of IoV system. As a supplement to cloud computing, mobile edge computing (MEC) can effectively solve the problem of long-distance transmission, bring the computing location close to the network edge of the mobile terminal, and improve the quality of service (QoS) of IoV. However, in the face of a large number of computing data, the congestion and waiting of the system can not be ignored. Finding the best offloading position of the task can effectively alleviate the above problems. Therefore, this paper proposes a joint on-board task offloading and job scheduling method based on cloud-edge computing (JVTR). Firstly, based on vehicle-to-vehicle (V2V) and vehicle location information, the task transmission route is obtained, and the offloading location is found. Then, the MEC server implements task scheduling and job scheduling according to the current status of virtual machines (VMs). We use ant colony optimization (ACO) to achieve multi-objective optimization, find the optimal offloading strategy, and evaluate the objective function with simple additive weighting (SAW) and multi-criteria decision making (MCDM). Finally, the effectiveness of JVTR is proved by experiments.
Keywords:
Internet of Vehicles (IoV)
mobile edge computing (MEC)
task offloading
ant colony optimization (ACO)
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