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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. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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