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 propose Deep Hybrid Policy Gradient (DHPG), a new Deep Reinforcement Learning (DRL) approach that operates in hybrid action spaces, enabling holistic decision-making and enhancing overall performance. We evaluated the performance of DHPG using simulations with a real-world C-V2N traffic dataset, comparing it to several state-of-the-art (SoA) solutions. DHPG outperforms these solutions, guaranteeing the $99^{th}$ percentile of C-V2N service delay target, 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质量，提高了车流效率，并降低了环境影响。为有效促进蜂窝车联网（C-V2N）服务的供给，我们解决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，我们对该联合问题进行了建模，并证明其在计算上不易处理。为应对其复杂性，我们提出了深度混合策略梯度（DHPG），这是一种在混合动作空间中运行的新型深度强化学习（DRL）方法，能够实现整体决策并提升综合性能。我们使用真实世界的C-V2N交通数据集进行仿真，评估了DHPG的性能，并将其与多种前沿（SoA）解决方案进行了比较。DHPG优于这些方案，在保证C-V2N服务延迟目标第99百分位数的同时，优化了计算资源的利用率。最后，通过时间复杂度分析验证了所提方法能够支持实时C-V2N服务。