北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2023, Vol. 46 ›› Issue (2): 78-83.

• 算力网络与分布式云 • 上一篇    下一篇

VEC中多边缘节点协作卸载与资源分配算法

彭维平,杨玉莹,宋成,阎俊豪   

  1. 河南理工大学
  • 收稿日期:2022-06-15 修回日期:2022-09-23 出版日期:2023-04-28 发布日期:2023-05-14
  • 通讯作者: 彭维平 E-mail:pwp9999@hpu.edu.cn
  • 基金资助:
    国家重点研发计划基金资助项目;河南省高校青年骨干教师计划资助项目

Cooperative Offloading and Resource Allocation Algorithm of Multi-edge Nodes in VEC

  • Received:2022-06-15 Revised:2022-09-23 Online:2023-04-28 Published:2023-05-14
  • Contact: Wei-Ping PENG E-mail:pwp9999@hpu.edu.cn
  • Supported by:
    The National Key Research and Development Program of China;The Foundation of the Young Key Teachers Program in Henan Universities

摘要: 针对车载边缘计算(VEC)中任务计算成本高以及边缘节点负载不均衡的问题,将软件定义网络(SDN)与多边缘计算相结合,构建了“端-多边-云”三层软件定义车载边缘计算(SDVEC)模型,并提出了一种多边缘节点协作卸载与资源分配算法(MCORA-KDQN)。由SDN控制器从全局角度获取网络信息,对任务卸载和资源分配进行统一调度。算法采用改进的K-Means算法确定任务的初始卸载决策,将任务分别划分到本地簇、边缘节点簇以及云服务器簇中,并利用深度Q网络(DQN)算法获得边缘节点簇中任务最优的卸载决策、卸载比例以及资源分配策略。仿真结果表明,相较于对比算法,所提算法的任务计算成本至少降低了18.6%,边缘节点的资源利用率至少提高了22.9%,且实现了边缘节点间的负载均衡。

关键词: 车载边缘计算, 软件定义网络, 协作卸载, 资源分配, K-Means, 深度Q网络

Abstract: Aiming at the problems of high computing cost of tasks and unbalanced load of edge nodes in vehicular edge computing (VEC), combined software defined network (SDN) with multi-edge computing, a three-layer software defined vehicular edge computing model of "end-multi-edge-cloud" (SDVEC) was constructed, and a multi-edge nodes cooperative offloading and resource allocation algorithm (MCORA-KDQN) was proposed. The SDN controller obtained network information from the global perspective, and uniformly scheduled task offloading and resource allocation. In the algorithm, the improved K-Means algorithm was adopted to divide the task into local cluster, edge nodes cluster and cloud server cluster respectively, in order to determine the initial offloading decision of the task, and then the deep q network (DQN) algorithm was used to obtain the optimal offloading decision, offloading proportion and resource allocation strategy of the task in the edge nodes cluster. The simulation results show that compared with the comparison algorithm, the proposed algorithm reduces the task computing cost by at least 18.6%, improves the resource utilization rate of edge nodes by at least 22.9%, and realizes the load balance among edge nodes.

Key words: vehicular edge computing, software defined network, cooperative offloading, resource allocation, K-Means, deep q network

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