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With the recent advancement of vehicular ad-hoc networks (VANETs) or the internet of vehicles (IoVs), vehicles are getting more powerful and generating huge amount of traffic data, including computation-intensive and delay-sensitive applications in the vehicular edge computing (VEC) networks, which are difficult to be processed by an individual vehicular node. These resource-demanding tasks can be transferred to another vehicular node with idle computing resources for processing. Due to high mobility and limited resources of vehicular nodes, it is challenging to execute lengthy computation-intensive tasks until completion within the delay constraint. There is a need to provide an efficient task offloading strategies to support these applications. In this paper, an efficient distributed task offloading scheme is proposed to select nearby vehicles with idle computing resources, to process the tasks in parallel by considering some vital metrics, including link reliability, distance, available computing resources, and relative velocity. In order to complete the lengthy computation-intensive tasks in vehicular edge computing networks, a task is divided into several subtasks before offloading. The performance of the proposed scheme is evaluated in several VEC network conditions. Results show that the proposed computation task offloading scheme achieves better performance in latency, throughput, resource utilization and packet delivery ratio than the existing schemes.

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