基于负载均衡的车辆到边缘映射优化算法在IoV中的攻击恢复
Optimizing Vehicle-to-Edge Mapping with Load Balancing for Attack-Resilience in IoV
摘要：
攻击恢复能力对于在执行关键任务的车联网（IoV）中保持持续的服务可用性至关重要。在本文中，我们解决了由于攻击边缘网络的服务中断的问题，并提出了一个攻击弹性映射车辆的边缘节点，主机不同类型的服务实例考虑资源效率和延迟。利用最优车辆到边缘（V2E）映射来执行（受攻击影响的边缘节点的）服务请求到多个无攻击边缘节点的分发。最优映射旨在以最小延迟改善用户体验，同时考虑边缘容量的公平使用和在不同边缘节点上发生故障时的平衡负载。所提出的映射解决方案中使用的深度强化学习（DRL）为基础的框架，以有效地处理服务请求和车辆移动性的动态。我们证明了所提出的映射方法的有效性，通过广泛的模拟结果，使用现实世界的车辆移动数据集从三个城市。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
