Deadline-Aware Task Offloading for Vehicular Edge Computing Networks using Traffic Lights Data

As vehicles becomes increasingly automated, novel vehicular applications are emerging to enhance the safety and security of the vehicles and improve user experience. This brings ever-increasing data and resource requirements for timely computation on the vehicle's on-board computing systems. To alleviate these demands, deploying vehicular edge computing (VEC) onto the road-side units (RSUs) have been proposed in prior work where vehicles can offload compute intensive tasks. Due to limited communication range of RSUs, vehicular movements influenced by traffic conditions impact the communication between the vehicles and the RSUs and can increase the response times of the offloaded applications. Existing task offloading strategies do not consider the influence of traffic lights on vehicular mobility while offloading workloads on the RSUs, and thereby cause deadline misses and quality-of-service (QoS) reduction. In this paper, we present a novel task model that captures time and location-specific requirements for vehicular applications. We then present a deadline-based strategy that incorporates traffic light data to opportunistically offload tasks. Our approach allows up to 33% more tasks to be offloaded onto the RSUs, compared to existing work, without causing any deadline misses and thereby maximizing the resource utilization on the RSUs.