论文标题
在启用攻击弹性的服务位置和启用边缘IOV网络中的可用性
On Attack-Resilient Service Placement and Availability in Edge-enabled IoV Networks
论文作者
论文摘要
在攻击公差和服务可用性方面，实现网络的弹性对于车辆互联网（IOV）网络至关重要，在这些网络中，车辆需要在敏感和关键安全应用（例如驾驶）方面提供帮助。在IOV流量的时变条件下，这尤其具有挑战性。在本文中，我们研究了一个攻击性的最佳服务放置问题，以确保向启用边缘的IOV网络中的用户提供无干扰的服务可用性。我们的工作旨在改善用户体验，同时最大程度地减少延迟，并同时考虑有限利用有限的边缘资源。首先，在考虑流量动态性并使用深度加固学习（DRL）框架时，执行了最佳服务位置。接下来，执行最佳的辅助映射和服务恢复位置，以说明边缘的攻击/失败。 DRL框架的使用有助于适应动态变化的IOV流量和服务需求。在这项工作中，我们开发了三种ILP模型，并将它们在基于DRL的框架中使用，以提供攻击弹性的服务放置，并确保具有有效的网络性能的服务可用性。进行了广泛的数值实验，以证明所提出的方法的有效性。
Achieving network resilience in terms of attack tolerance and service availability is critically important for Internet of Vehicles (IoV) networks where vehicles require assistance in sensitive and safety-critical applications like driving. It is particularly challenging in time-varying conditions of IoV traffic. In this paper, we study an attack-resilient optimal service placement problem to ensure disruption-free service availability to the users in edge-enabled IoV network. Our work aims to improve the user experience while minimizing the delay and simultaneously considering efficient utilization of limited edge resources. First, an optimal service placement is performed while considering traffic dynamicity and meeting the service requirements with the use of a deep reinforcement learning (DRL) framework. Next, an optimal secondary mapping and service recovery placements are performed to account for the attacks/failures at the edge. The use of DRL framework helps to adapt to dynamically varying IoV traffic and service demands. In this work, we develop three ILP models and use them in the DRL-based framework to provide attack-resilient service placement and ensure service availability with efficient network performance. Extensive numerical experiments are performed to demonstrate the effectiveness of the proposed approach.
