吉林大学学报(理学版) ›› 2026, Vol. 64 ›› Issue (2): 411-0420.

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车辆边缘计算中的资源分配与任务卸载比例优化

王子恒1, 唐菁敏1, 宋耀莲1, 虞贵财2   

  1. 1. 昆明理工大学 信息工程与自动化学院, 昆明 650504; 2. 青海民族大学 物理与信息工程学院, 西宁 810007
  • 收稿日期:2024-11-14 出版日期:2026-03-26 发布日期:2026-03-26
  • 通讯作者: 唐菁敏 E-mail:948129132@qq.com

Resource Allocation and Task Offloading Ratio Optimization in Vehicular Edge Computing

WANG Ziheng1, TANG Jingmin1, SONG Yaolian1,  YU Guicai2   

  1. 1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504, China;
    2. School of Physics and Information Engineering, Qinghai Minzu University, Xining 810007, China
  • Received:2024-11-14 Online:2026-03-26 Published:2026-03-26

摘要: 针对当前车联网边缘计算中未充分考虑车辆与基站通信中断以及将任务卸载到单一节点计算导致资源浪费的问题, 提出一种基于模拟退火算法(SAA)的车联网资源优化方案. 在考虑到通信保持时间及任务卸载计算时效的前提下, 均衡系统时延和能耗建模最小化系统效用问题, 再将其分解为资源分配与任务卸载比例优化子问题解决. 首先, 利用Lagrange乘子法和拟凸优化技术解决资源分配子问题; 其次, 采用SAA对任务卸载比例与资源分配子问题联合优化. 仿真实验结果表明, 该方案可有效降低系统平均时延、 能耗和效用, 并有较好的收敛性, 可解决车载网络中各种应用延迟敏感、 能耗高及系统开销大的问题. 

关键词: 车联网, 资源分配, 联合优化, 模拟退火算法

Abstract: Aiming at the problem that the communication interruptions between vehicles and base stations was not fully considered in current vehicular networks with edge computing, and  the resource wastage caused by offloading tasks to a single node for computing, we proposed a vehicular network resource optimization scheme based on the simulated annealing algorithm (SAA). Taking into account the communication retention time and task offloading computation efficiency, we modelled the problem of minimizing the system utility by balancing system latency and energy consumption, and then decomposed it into subproblems of resource allocation and task offloading ratio optimization to solve. Firstly, the resource allocation subproblem was solved by using the Lagrange multiplier method and quasi-convex optimization techniques. Secondly, the SAA was used to jointly optimize the task offloading ratio and resource allocation subproblems. Simulation experiment results show that the proposed scheme can effectively reduce  average latency, energy consumption, and utility of the system, and has good convergence, which  can solve  the problems of latency sensitivity, high energy consumption, and high system overhead in vehicular networks for various applications.

Key words: vehicular networks (V2X), resource allocation, joint optimization, simulated annealing algorithm (SAA)

中图分类号: 

  • TP929.5