Abstract: Systems and methods described herein relate to generating a task offloading strategy for a vehicular edge-computing environment. One embodiment simulates a vehicular edge-computing environment in which one or more vehicles perform computational tasks whose data is partitioned into segments and performs, for each of a plurality of segments, a Deep Reinforcement Learning (DRL) training procedure that includes receiving state-space information regarding the one or more vehicles and one or more intermediate network nodes; inputting the state-space information to a policy network; generating, from the policy network, an action concerning a current segment; and assigning a reward to the policy network for the action in accordance with a predetermined reward function. This embodiment produces, via the DRL training procedure, a trained policy network embodying an offloading strategy for segmentation offloading of computational tasks from vehicles to one or more of an edge server and a cloud server.

Abstract: System, methods, and other embodiments described herein relate to improving scheduling tasks within an edge-computing environment. In one embodiment, a method includes, upon establishing a communication connection with a vehicle by an edge device of the edge-computing environment, collecting offloading information about the vehicle including task information describing at least a vehicle task that is to be offloaded to the edge device and context information about aspects relating to operation of the vehicle. The method includes triggering offloading of the vehicle task to the edge device in response to determining that at least the context information satisfies a scheduling threshold. The method includes providing, by the edge device, a result of executing the vehicle task to the vehicle.