A Lightweight Greedy Task Offloading Algorithm for Vehicular Edge Computing Networks

Modern vehicles require a significant processing power along with storage space. To address this need, offloading the vehicular tasks to adjacent edge servers has been proposed. However, even the considerable processing power of edge servers may not be sufficient to cope with the increasing demand of vehicular computational tasks, leading to potential failure to complete tasks within their deadlines. Vehicular Edge Computing (VEC) has emerged as a promising approach to provide these necessary computational and storage needs through powerful edge servers together with the resources of the nearby vehicles, whereby improving latency for delay-sensitive tasks. However, this heterogeneous processing environment requires a suitable task offloading and scheduling mechanism. This paper introduces a lightweight greedy task offloading algorithm, addressing this requirement by considering the possibility of collaboration between edge servers and utilizing the idle resources of parked vehicles within the edge servers' coverage area. A system solution for efficient use of resources in vehicles and edge servers will be presented. The task offloading problem is modeled as an optimization problem with the objective of maximizing the task completion ratio (TCR), which will be solved using a two-layer greedy approach. Simulation results demonstrate that, on average, the proposed task offloading method can improve the TCR by a factor of 1.29.