Resource Provision for Cloud-Enabled Automotive Vehicles With a Hierarchical Model
提出一种分层、去中心化的拍卖式资源分配模型，用于云赋能汽车在高层竞价、在低层优化车载资源分配，并证明纳什均衡的存在性与唯一性。
Cloud computing is an emerging paradigm to enable computation and data-intensive automotive systems for improved safety and drivability. In this article, we propose a hierarchical, decentralized, and auction-based resource allocation model for cloud-enabled automotive vehicles. In this model, cloud-enabled vehicles bid for resources at a high level, inducing a multiplayer game; at a low level, each vehicle performs an onboard resource optimization to allocate its obtained resources to its cloud-based applications. The Nash equilibrium of the induced game is defined, and we show the existence and uniqueness of the equilibrium. A constrained optimization problem is solved for onboard resource allocation. A distributed update mechanism is considered: asynchronized update where only a subset of vehicles updates their bid at each iteration. This mechanism shares desired features of requiring little communication and being secure. Convergence to Nash equilibrium is proved for the proposed update mechanism. Furthermore, the robustness to stochastic task arrival rate is characterized in terms of total variance distance. Numerical simulations are presented to demonstrate the efficacy of the proposed framework.