论文标题
通过负载平衡优化车辆到边缘地图，以实现IOV的攻击弹性
Optimizing Vehicle-to-Edge Mapping with Load Balancing for Attack-Resilience in IoV
论文作者
论文摘要
攻击弹性对于维持执行关键任务的车辆互联网（IOV）的连续服务可用性至关重要。在本文中，我们解决了由于在边缘网络上的攻击而引起的服务中断问题，并提出了对启动弹性映射到边缘节点的攻击绘制，该节点托管了考虑资源效率和延迟的不同类型的服务实例。使用最佳的车辆到边缘（V2E）映射执行服务请求（受攻击影响的边缘节点的分布（攻击影响边缘节点）。最佳映射旨在以最小的延迟来改善用户体验，同时考虑到边缘能力和平衡负载在不同边缘节点上的故障时的平衡负载。所提出的映射解决方案用于基于深的加固学习（DRL）框架中，以有效地处理服务请求和车辆移动性的动力。我们使用来自三个城市的现实世界移动性数据集通过广泛的模拟结果来证明所提出的映射方法的有效性。
Attack-resilience is essential to maintain continuous service availability in Internet of Vehicles (IoV) where critical tasks are carried out. In this paper, we address the problem of service outage due to attacks on the edge network and propose an attack-resilient mapping of vehicles to edge nodes that host different types of service instances considering resource efficiency and delay. The distribution of service requests (of an attack-affected edge node) to multiple attack-free edge nodes is performed with an optimal vehicle-to-edge (V2E) mapping. The optimal mapping aims to improve the user experience with minimal delay while considering fair usage of edge capacities and balanced load upon a failure over different edge nodes. The proposed mapping solution is used within a deep reinforcement learning (DRL) based framework to effectively deal with the dynamism in service requests and vehicle mobility. We demonstrate the effectiveness of the proposed mapping approach through extensive simulation results using real-world vehicle mobility datasets from three cities.
