Vehicular task scheduling strategy with resource matching computing in cloud-edge collaboration

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.

Automated summary

In this study, a strategy for scheduling tasks on on board units (OBUs) using multi-access edge cloud (MEC) and remote cloud was proposed to minimize completion time and computing unit number. A multi-objective optimization model considering tasks and cloud-edge collaboration was introduced, followed by a task scheduling strategy based on resource matching degree. An improved hybrid genetic algorithm was utilized to obtain better solutions by considering resource matching measure between tasks and computing units. The numerical results demonstrated the effectiveness of the proposed strategy.