一种数字孪生辅助聚类的车辆边缘计算任务卸载方法

A digital twin assisted and clustering based task offloading method for vehicle edge computing

关键词:  车辆边缘计算  数字孪生网络  社交关系模型  车辆聚类  深度强化学习  动态任务卸载

Key words:  vehicle edge computing  digital twin network  social relationship model  vehicle clustering  deep reinforcement learning  dynamic task offloading

摘要:

为解决车辆边缘计算中因高动态网络拓扑结构、高维动作探索空间以及严格的低时延约束引起的任务卸载不稳定、计算资源不能充分利用、卸载成本高、用户服务质量低的问题,本文提出了一种数字孪生辅助聚类的车辆边缘计算任务卸载方法。首先构建了车辆社交关系模型,以量化车辆间的相互关系,通过引入两个社交信任因子衡量车辆间卸载稳定性,减少因通信链路不稳定而造成的计算资源浪费；而后构建数字孪生车辆边缘计算网络模型,通过数字孪生网络模型与实体车载边缘设备网络之间的双向信息交互,实时监控车辆边缘设备的状态；同时,设计了基于引力模型的聚类算法,辅助车辆聚类以缩小动作探索空间,提高计算任务的卸载效率,降低边缘任务计算成本；最后,在以上优化策略的基础上,设计实现了数字孪生辅助聚类的双延迟深度确定性策略梯度边缘计算任务卸载算法。仿真对比实验表明,与已有的卸载方法相比,所提方法在充分利用计算资源的基础上,能够显著减小任务卸载成本,降低任务执行时延并提高任务卸载成功率,可以实现车辆边缘计算任务的高效稳定卸载,提升用户服务质量。

Abstract:

To address the issues of task offloading instability, underutilization of computing resources, high offloading costs, and low user service quality in vehicle edge computing, which are caused by highly dynamic network topologies, high-dimensional action exploration spaces, and strict low-latency constraints, a digital twin-assisted clustering method for vehicle edge computing task offloading is proposed. Firstly, a vehicle social relationship model is constructed to quantify the relationships between vehicles. By introducing two social trust factors to measure offloading stability between vehicles, the method reduces the waste of computing resources caused by unstable communication links. Next, a digital twin-vehicle edge computing network model is constructed. Through the bidirectional information interaction between the digital twin network model and the physical vehicle edge device network, the condition of vehicle edge devices is monitored in real time. In addition, a clustering algorithm based on the gravity model is designed to assist vehicle clustering, thus narrowing the space for action exploration, improving the efficiency of computing task offloading, and reducing the cost of edge task computation. Finally, based on the above optimization strategies, a dual-latency deep deterministic policy gradient edge computing task offloading algorithm assisted by digital twin clustering is designed and implemented. Simulation experiments demonstrate that, compared to existing offloading methods, the proposed method significantly reduces task offloading costs, decreases task execution latency, and improves task offloading success rates while fully utilizing computing resources. This enables the efficient and stable offloading of vehicle-edge computing tasks, thereby enhancing user service quality.