Stackelberg Game-Based Dependency-Aware Task Offloading and Resource Pricing in Vehicular Edge Networks

Vehicular Edge Computing (VEC) is an effective paradigm in Internet of Vehicles, which allows vehicles to offload delay-sensitive tasks to nearby Road Side Units (RSUs) for processing, thereby enhancing the Quality of Service (QoS). However, the SDN controller that manages RSUs often have individual rationality and selfishness, and thus is unwilling to provide free computation resources to vehicles. Meanwhile, the dependency relationships among vehicular subtasks are not well investigated, resulting in unsatisfactory task latency and energy consumption. In order to effectively motivate the selfish SDN controller to participate in computation offloading and comprehensively consider all dependency situations among multiple subtasks, this paper proposes a Stackelberg game-based Dependency-aware task Offloading and resource Pricing framework (SDOP). Specifically, we first model the interaction between the SDN controller and vehicles as a Stackelberg game, where both parties wish to maximize their utility. Then, we employ the backward induction approach to analyze the investigated problem, and prove the existence and uniqueness of Nash and Stackelberg equilibrium. Next, we propose a Gradient Ascent Plus Genetic algorithm (GAPG) to solve the considered problem. Finally, extensive simulation results show that the proposed GAPG can significantly improve the utility of both the SDN controller and vehicles under various scenarios, when compared with other baseline schemes.