面向车辆边缘计算的任务合作卸载

随着物联网技术和人工智能技术的飞速发展，车辆边缘计算越来越引起人们的注意。车辆如何有效地利用车辆周边的各种通信、计算和缓存资源，结合边缘计算系统模型将计算任务迁移到离车辆更近的路边单元，已经成为目前车联网研究的热点。由于车辆应用设备有限的计算资源，车辆用户的任务计算需求无法满足，需要充分利用车辆周边的计算资源来计算任务。本文研究了车辆边缘计算中任务的合作卸载机制，以最小化车辆任务的计算时延。首先，设计了任务合作卸载的三层系统架构，考虑了车辆周边停泊车辆的计算资源以及路边单元的计算资源，组成云服务器层、停泊车辆合作集群层和路边单元合作集群层的三层架构。通过路边单元合作集群和停泊车辆合作集群的合作卸载，充分利用系统的空闲计算资源，进一步提高了系统的资源利用率。然后，基于k-聚类算法的思想提出了路边单元合作集群划分算法对路边单元进行合作集群的划分，并采用块连续上界最小化的分布式迭代优化方法设计了任务合作卸载算法,对终端车辆用户的任务进行卸载计算。最后，通过将本文算法和其他算法方案的进行实验对比，仿真结果表明，本文算法在系统时延和系统吞吐量方面具有更好的性能表现，可以降低23%的系统时延，并且能提升28%的系统吞吐量。

Task Cooperative Offloading for Vehicle Edge Computing

With the rapid development of IoT technology and artificial intelligence technology, Vehicle Edge Computing has attracted more and more attention. Effectively utilizing the various communication, computational, and caching resources in the vicinity of vehicles, and employing edge computing system models to migrate computational tasks closer to the vehicles, have become a hotspot in current Internet of Vehicles research. Due to the limited computational resources of in-vehicle devices, the computational demands of vehicle users cannot be met without making full use of the computational resources available in the vicinity of vehicles. This paper investigated a collaborative offloading mechanism for computational tasks in vehicle edge computing, aiming to minimize the computational latency of vehicular tasks. Firstly, a three-layer architecture for task collaborative offloading was designed considering the computational resources of parked vehicles in the vicinity of vehicles as well as the computational resources of roadside units, which is comprised with three tiers: cloud server layer, roadside unit collaboration cluster layer, and the parked vehicle collaboration cluster layer. By means of collaborative offloading between the roadside unit collaboration cluster and the parked vehicle collaboration cluster, the free computational resources of system were fully leveraged, further enhanced resource utilization. Then, this paper proposed a roadside unit collaboration cluster partitioning algorithm based on k-means clustering algorithm for segmenting roadside units into collaboration clusters. A distributed iterative optimization approach with block-coordinate upper-bound minimization was utilized to design a task collaborative offloading algorithm for offloading the computation of terminal vehicle users'' tasks. Finally, By comparing the algorithm proposed in this paper with other algorithm schemes through experiments, the algorithm proposed in this paper had better performance in terms of system latency and system throughput according the stimulation result. Specifically, system latency was reduced by 23% and system throughput was increased by 28%.