Cellular-Vehicle-to-Everything (C-V2X) is currently at the forefront of the digital transformation of our society. By enabling vehicles to communicate with each other and with the traffic environment using cellular networks, we redefine transportation, improving road safety and transportation services, increasing efficiency of traffic flows, and reducing environmental impact. This paper proposes a decentralized approach for provisioning Cellular Vehicular-to-Network (C-V2N) services, addressing the coupled problems of service task placement and scaling of edge resources. We formalize the joint problem and prove its complexity. We propose an approach to tackle it, linking the two problems, employing decentralized decision-making using (i) a greedy approach for task placement and (ii) a Deep Deterministic Policy Gradient (DDPG) based approach for scaling. We benchmark the performance of our approach, focusing on the scaling agent, against several State-of-the-Art (SoA) scaling approaches via simulations using a real C-V2N traffic data set. The results show that DDPG-based solutions outperform SoA solutions, keeping the latency experienced by the C-V2N service below the target delay while optimizing the use of computing resources. By conducting a complexity analysis, we prove that DDPG-based solutions achieve runtimes in the range of sub-milliseconds, meeting the strict latency requirements of C-V2N services.

翻译：蜂窝车联网（C-V2X）目前处于社会数字化转型的前沿。通过利用蜂窝网络使车辆之间及车辆与交通环境之间能够通信，我们重新定义了交通运输，提升了道路安全与运输服务，提高了交通流效率，并减少了对环境的影响。本文提出了一种去中心化的方法来实现蜂窝车辆到网络（C-V2N）服务的供给，解决了服务任务放置与边缘资源扩展这两个耦合问题。我们对这一联合问题进行了形式化描述并证明了其复杂性。我们提出了一种解决该问题的方法，将这两个问题联系起来，采用去中心化决策：（i）一种用于任务放置的贪婪方法，以及（ii）一种基于深度确定性策略梯度（DDPG）的扩展方法。我们通过使用真实C-V2N交通数据集进行仿真，将我们的方法（重点聚焦于扩展智能体）的性能与多种最先进的（SoA）扩展方法进行了基准测试。结果表明，基于DDPG的解决方案优于SoA解决方案，能够在保持C-V2N服务所经历延迟低于目标时延的同时，优化计算资源的利用。通过复杂性分析，我们证明了基于DDPG的解决方案可实现亚毫秒级的运行时间，满足了C-V2N服务的严格延迟要求。