Efficient Workload Allocation and User-Centric Utility Maximization for Task Scheduling in Collaborative Vehicular Edge Computing

Abstract

By integrating Mobile Edge Computing (MEC) into vehicular networks, vehicular edge computing extends computing capability to the vehicular network edge and hosts services in close proximity of connected vehicles. Parked Vehicles (PVs) occupy a large portion of the global vehicle and have idle states and resources. They collaborate with the MEC servers for cooperative task processing. This gives rise to a new computing paradigm, called by Collaborative Vehicular Edge Computing (CVEC). In CVEC, we introduce an offloading service provider that deploys an MEC server and schedules PVs on demand to handle offloading tasks. Efficient workload allocation and user-centric utility maximization are studied to optimize the network-wide task scheduling. In dynamic environment, offloading destination of each task is determined in a probabilistic manner. When necessary, the offloading service provider represents an offloading user to design a contract based incentive mechanism for the PVs. Based on contract theory and prospect theory, we model the offloading user's subjective evaluations on the utility in computation offloading, and derive an optimal contract to maximize the subjective utility under information asymmetry. Finally, numerical results are provided to demonstrate the effectiveness and efficiency of our scheme.