LARS: A Latency-Aware and Real-Time Scheduling Framework for Edge-Enabled Internet of Vehicles

With the development of Internet of Things and mobile computing, the explosive proliferation of latency-sensitive applications raises high computation demands for mobile devices. To this end, offloading computation of applications to edge-enabled Internet of Vehicles (IoV) has emerged as an effective solution. However, most of the existing studies on this issue assume that IoV can be easily formed in the practical environment, and neglect the dependency relationship between tasks of the offloading application. In this paper, we first give several observations based on the analysis results of the real traffic dataset to verify the feasibility of aggregating vehicular resources in the real world. Then, we design a Latency-aware Real-time Scheduling Framework for the edge-enabled IoV, named LARS, in which mobile users can offload applications to LARS, and the offloading tasks can be scheduled to the appropriate vehicular resources in real-time. First, we propose a clustering-based algorithm to generate Herds, which treats connected vehicles as edge computation resources to provide cooperative computing services. Second, considering the dependency relationship between tasks in the job, we present a greedy-based task scheduling algorithm for offloading jobs, the objective of which is to minimize the total latency of the job as well as maximize the resource utilization of Herds. The simulation experiment based on the real traffic dataset shows that Herds generated by the proposed clustering-based algorithm can maintain a stable period to provide computing service, and the experiments on testbed include two case studies demonstrate that the superiority of the proposed scheme compared to baselines, in terms of latency and resource utilization.