Optimized Assignment of Computational Tasks in Vehicular Micro Clouds

The ever-increasing advancements of vehicles have not only made them mobile devices with Internet connectivity, but also have pushed vehicles to become powerful computing resources. To this end, a cluster of vehicles can form a vehicular micro cloud, creating a virtual edge server and providing the computational resources needed for edge-based services. In this paper, we study the assignment of computational tasks among micro cloud vehicles of different computing resources. In particular, we formulate a bottleneck assignment problem, where the objective is to minimize the completion time of tasks assigned to available vehicles in the micro cloud. A two-stage algorithm, with polynomial-time complexity, is proposed to solve the problem. We use Monte Carlo simulations to validate the effectiveness of the proposed algorithm in two micro cloud scenarios: a parking structure and an intersection in Manhattan grid. It is shown that the algorithm significantly outperforms random assignment in completion time. For example, compared to the proposed algorithm, the completion time is 3.6x longer with random assignment when the number of cars is large, and it is 2.1x longer when the tasks have more varying requirements.