Vehicular edge computing (VEC) is an effective paradigm in Internet of Vehicles (IoV), 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 software defined networking (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 article 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.

中文翻译：

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车辆边缘网络中基于 Stackelberg 游戏的依赖感知任务卸载和资源定价


车辆边缘计算（VEC）是车联网（IoV）中的一种有效范例，它允许车辆将延迟敏感的任务卸载到附近的路边单元（RSU）进行处理，从而提高服务质量（QoS）。然而，管理RSU的软件定义网络（SDN）控制器往往具有个体理性和自私性，不愿意为车辆提供免费的计算资源。同时，车辆子任务之间的依赖关系没有得到很好的研究，导致任务延迟和能耗不理想。为了有效激励自私SDN控制器参与计算卸载并综合考虑多个子任务之间的所有依赖情况，本文提出了一种基于Stackelberg博弈的依赖感知任务卸载和资源定价框架（SDOP）。具体来说，我们首先将 SDN 控制器和车辆之间的交互建模为 Stackelberg 游戏，双方都希望最大化其效用。然后，我们采用逆向归纳法来分析所研究的问题，并证明了纳什均衡和斯塔克尔伯格均衡的存在性和唯一性。接下来，我们提出梯度上升加遗传算法（GAPG）来解决所考虑的问题。最后，大量的仿真结果表明，与其他基线方案相比，所提出的 GAPG 可以在各种场景下显着提高 SDN 控制器和车辆的效用。