As a promising technology, vehicular edge computing (VEC) can provide computing and caching services by deploying VEC servers near vehicles. However, VEC networks still face challenges such as high vehicle mobility. Digital twin (DT), an emerging technology, can predict, estimate, and analyze real-time states by digitally modeling objects in the physical world. By integrating DT with VEC, a virtual vehicle DT can be created in the VEC server to monitor the real-time operating status of vehicles. However, maintaining the vehicle DT model requires ongoing attention from the VEC server, which also needs to offer computing services for the vehicles. Therefore, effective allocation and scheduling of VEC server resources are crucial. This study focuses on a general VEC network with a single VEC service and multiple vehicles, examining the two types of delays caused by twin maintenance and computational processing within the network. By transforming the problem using satisfaction functions, we propose an optimization problem aimed at maximizing each vehicle's resource utility to determine the optimal resource allocation strategy. Given the non-convex nature of the issue, we employ multi-agent Markov decision processes to reformulate the problem. Subsequently, we propose the twin maintenance and computing task processing resource collaborative scheduling (MADRL-CSTC) algorithm, which leverages multi-agent deep reinforcement learning. Through experimental comparisons with alternative algorithms, it demonstrates that our proposed approach is effective in terms of resource allocation.

翻译：车载边缘计算（VEC）作为一种前景广阔的技术，可通过在车辆附近部署VEC服务器来提供计算与缓存服务。然而，VEC网络仍面临车辆高移动性等挑战。数字孪生（DT）作为新兴技术，能够通过对物理世界对象进行数字化建模，以预测、估计和分析其实时状态。通过将DT与VEC相结合，可在VEC服务器中创建虚拟车辆DT以监测车辆的实时运行状态。然而，维护车辆DT模型需要VEC服务器持续投入资源，而该服务器同时还需为车辆提供计算服务。因此，对VEC服务器资源进行有效的分配与调度至关重要。本研究聚焦于包含单个VEC服务器与多辆车辆的一般性VEC网络，探讨网络中因孪生维护与计算处理引发的两类时延问题。通过采用满意度函数对问题进行转化，我们提出了一个旨在最大化每辆车资源效用的优化问题，以确定最优资源分配策略。鉴于该问题的非凸特性，我们采用多智能体马尔可夫决策过程对问题进行重构。随后，我们提出了基于多智能体深度强化学习的孪生维护与计算任务处理资源协同调度（MADRL-CSTC）算法。通过与替代算法的实验对比，结果表明我们提出的方法在资源分配方面具有显著有效性。