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 vehicular traffic flows, and reducing environmental impact. To effectively facilitate the provisioning of Cellular Vehicular-to-Network (C-V2N) services, we tackle the interdependent problems of service task placement and scaling of edge resources. Specifically, we formulate the joint problem and prove that it is not computationally tractable. To address its complexity we introduce a Deep Hybrid Policy Gradient (DHPG), a Deep Reinforcement Learning (DRL) approach for hybrid action spaces.The performance of DHPG is evaluated against several state-of-the-art (SoA) solutions through simulations employing a real-world C-V2N traffic dataset. The results demonstrate that DHPG outperforms SoA solutions in maintaining C-V2N service latency below the preset delay threshold, while simultaneously optimizing the utilization of computing resources. Finally, time complexity analysis is conducted to verify that the proposed approach can support real-time C-V2N services.

翻译：蜂窝车联网（C-V2X）当前正处于社会数字化转型的前沿。通过使车辆能够利用蜂窝网络彼此通信并与交通环境交互，我们重新定义了交通运输，提升了道路安全与交通服务水平，提高了车辆交通流的效率，并减少了环境影响。为有效促进蜂窝车对网络（C-V2N）服务的供给，我们研究了服务任务放置与边缘资源扩展这两个相互依赖的问题。具体而言，我们将该联合问题建模并证明其在计算上是难以处理的。为应对其复杂性，我们提出了一种深度混合策略梯度（DHPG），这是一种面向混合动作空间的深度强化学习（DRL）方法。通过采用真实世界的C-V2N交通数据集进行仿真，我们将DHPG的性能与多种前沿（SoA）解决方案进行了比较评估。结果表明，DHPG在将C-V2N服务延迟维持在预设时延阈值以下的同时，能优化计算资源利用率，其性能优于SoA解决方案。最后，我们进行了时间复杂度分析，以验证所提方法能够支持实时C-V2N服务。