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.
翻译:在本文件中,我们处理边缘网络受到攻击而造成服务中断的问题,并提议对车辆进行具有攻击弹性的绘图,以在有资源效率和延迟的情况下,将容纳不同类型服务的节点的车辆置于边缘,以考虑到资源效率和延迟。将服务请求(受攻击影响的边缘节点)分配给多个不受攻击的边缘节点,以最佳的车辆对前沿(V2E)绘图方式进行。最佳的绘图旨在尽可能及时地改善用户的经验,同时考虑公平使用边缘能力,并在不同边缘节点失灵时平衡负荷。拟议的绘图解决方案在基于深度强化学习(DRL)的框架内使用,以有效处理服务请求中的活力和车辆机动性。我们通过使用三个城市的实时车辆移动数据集,通过广泛的模拟结果,展示了拟议的绘图方法的有效性。