论文标题
车辆网络的数字双驱动计算资源管理
Digital Twin-Driven Computing Resource Management for Vehicular Networks
论文作者
论文摘要
本文提出了一种基于数字双胞胎和人工智能（AI）的车辆网络中边缘服务器资源管理的新方法。具体而言，我们构建了针对车辆网络量身定制的两层数字双胞胎，以捕获车辆和边缘服务器的网络相关功能。通过利用此类功能，我们提出了一个两阶段的计算资源分配方案。首先，中央控制器会根据网络动力学和服务需求定期生成用于实时计算资源分配的参考策略，该要求由Edge Server的数字双胞胎捕获。其次，边缘服务器的计算资源通过符合参考策略的基于低复杂性匹配的分配将实时分配给单个车辆。通过利用数字双胞胎，建议的方案可以以可扩展的方式适应动态服务需求和车辆移动性。仿真结果表明，所提出的数字双驱动方案使车辆网络比基准方案支持更多的计算任务。
This paper presents a novel approach for computing resource management of edge servers in vehicular networks based on digital twins and artificial intelligence (AI). Specifically, we construct two-tier digital twins tailored for vehicular networks to capture networking-related features of vehicles and edge servers. By exploiting such features, we propose a two-stage computing resource allocation scheme. First, the central controller periodically generates reference policies for real-time computing resource allocation according to the network dynamics and service demands captured by digital twins of edge servers. Second, computing resources of the edge servers are allocated in real time to individual vehicles via low-complexity matching-based allocation that complies with the reference policies. By leveraging digital twins, the proposed scheme can adapt to dynamic service demands and vehicle mobility in a scalable manner. Simulation results demonstrate that the proposed digital twin-driven scheme enables the vehicular network to support more computing tasks than benchmark schemes.
