Vehicle-Road Cooperative Task Offloading with Task Migration in MEC-Enabled IoV

Mobile edge computing (MEC) is considered as a key technology for addressing computation-intensive and delay-critical applications in the Internet of vehicles (IoV). In MEC-enabled IoV, vehicles lighten their computing load by offloading tasks to edge servers. However, the high speed mobility of vehicles and time-varying network environment brings tough challenges to task offloading. In addition, considering only roadside units (RSUs) or vehicles as offloading objects lead to the waste of computing resources and increase the process delay of task. To this end, we formulate the reduction of task processing delay and improvement of service reliability as an utility maximization problem and propose a distributed vehicle-road cooperative task offloading scheme with task migration. Then we use RSUs and surrounding vehicles as offloading objects and divide offloading tasks into multiple subtasks for offloading objects and local parallel processing, which improves the utilization rate of computing resources. Meanwhile, we reduce the task processing failure by migrating the computing results of offloading subtasks. The offloading scheme is formulated as a mixed-integer nonlinear optimization problem, and a multi-agent deep Q-learning network (MADQN) algorithm is proposed to find the near-optimal offloading objects and number of offloading subtasks. Simulation results show that the proposed approach significantly improves the total task processing speed and service reliability.