The rapid development of emerging vehicular edge computing (VEC) brings new opportunities and challenges for dynamic resource management. The increasing number of edge data centers, roadside units (RSUs), and network devices, however, makes resource management a complex task in VEC. On the other hand, the exponential growth of service applications and end-users makes corresponding QoS hard to maintain. Intent-Based Networking (IBN), based on Software-Defined Networking, was introduced to provide the ability to automatically handle and manage the networking requirements of different applications. Motivated by the IBN concept, in this paper, we propose a novel approach to jointly orchestrate networking and computing resources based on user requirements. The proposed solution constantly monitors user requirements and dynamically re-configures the system to satisfy desired states of the application. We compared our proposed solution with the state-of-the-art networking embedding algorithms using real-world taxi GPS traces. Results show that our proposed method is significantly faster (up to 95%) and can improve resource utilization (up to 76%) and the acceptance ratio of computing and networking requests with various priorities (up to 71%). We also present a small-scale prototype of the proposed intent management framework to validate our solution.

翻译：新兴车载边缘计算的快速发展为动态资源管理带来了新的机遇与挑战。然而，边缘数据中心、路侧单元（RSU）及网络设备数量的激增，使得车载边缘计算中的资源管理成为一项复杂任务。另一方面，服务应用与终端用户的指数级增长导致相应服务质量（QoS）难以维持。基于软件定义网络的意图驱动网络被引入，以提供自动处理和管理不同应用网络需求的能力。受意图驱动网络概念的启发，本文提出一种基于用户需求联合编排网络与计算资源的新方法。该方案持续监控用户需求并动态重构系统，以满足应用的期望状态。我们利用真实出租车GPS轨迹数据，将所提方案与最新网络嵌入算法进行对比。结果表明，本方法速度显著提升（最高达95%），能提高资源利用率（最高达76%），并提升不同优先级计算与网络请求的接受率（最高达71%）。此外，我们通过构建所提议图管理框架的小规模原型验证了该方案的可行性。