VEC中多边缘节点协作卸载与资源分配算法

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

针对车载边缘计算(VEC)中任务计算成本高和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”3层软件定义车载边缘计算模型,并提出了一种新的协作卸载和资源分配算法。使用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。将改进的k-means算法用于确定任务的初始卸载决策,将任务分别分配到本地簇、边缘节点簇和云服务器簇中。此外,利用深度Q网络算法获得了边缘节点簇中任务最优的卸载决策、卸载比例和资源分配策略。仿真实验结果表明,相较于对比算法,用所提算法使任务的计算成本降低了18.6%以上,提高了22.9%以上的边缘节点资源利用率,并实现了边缘节点间的负载均衡。

To solve the issues 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”is constructed,and a multi-edge nodes cooperative offloading and resource allocation algorithm is proposed.The SDN controller is applied to obtain network information from the global perspective,and uniformly scheduled task offloading and resource allocation.The improved k-means algorithm is adopted to divide the task into the local cluster,edge nodes cluster and cloud server cluster respectively.To determine the initial offloading decision of the task,the deep Q network algorithm is 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 baseline algorithm,the proposed algorithm reduces the task computing cost by more than 18.6%,improves the resource utilization rate of edge nodes by more than 22.9%,and realizes the load balance among edge nodes.