摘要
边缘算力融合网络将算力下沉至近用户侧,通过分布式边缘算力节点相互协同以在本地完成计算任务,显著降低云端负担和传输时延。然而,随着用户接入密度提高和场景复杂化,如何动态优化网络资源以协同应对多样化服务需求和大规模数据处理任务成为重大挑战。因此,提出了一种基于双深度Q网络(double deep Q network,DDQN)边缘算力融合网络资源管理方法,结合虚拟网络嵌入(virtual network embedding,VNE)技术,建立了以长期资源收益成本比最大化为目标的多约束优化模型。通过DDQN架构的在线学习能力,利用环境交互反馈实现动态优化决策。仿真实验表明,该方法在虚拟网络请求(virtual network request,VNR)接受成功率、长期嵌入收益和长期嵌入收益成本比3个指标上,较现有方法分别提升了13.3%、25.7%和8.5%。
The edge computing fusion network sinks the computing resources to the user side and completes the computing tasks locally through the coordination of distributed edge computing nodes,which significantly reduces the cloud burden and transmission delay.However,with the increase of user access density and the complexity of scenarios,how to dynamically optimize network resources to cope with diversified service demands and large-scale data processing tasks has become a major challenge.Therefore,a resource management method for edge computing networks based on double deep Q network(DDQN)was proposed.Integrating the virtual network embedding(VNE)technology,the proposed method formulated a multi-constraint optimization model to maximize the long-term embedding revenue-to-cost ratio.By leveraging the online learning capabilities of the DDQN framework,it enabled dynamic decision-making through interaction and feedback with the environment.Simulation results demonstrate that the proposed method achieves average improvements of 13.3%,25.7%and 8.5%of virtual network request(VNR)acceptance rate,long-term embedding revenue,and long-term revenue-to-cost ratio,respectively,compared with the existing methods.
作者
董玉池
闫亚旗
冉沛
王东
张阔
张文龙
DONG Yuchi;YAN Yaqi;RAN Pei;WANG Dong;ZHANG Kuo;ZHANG Wenlong(China Tower Co.,Ltd.,Beijing 100080,China)
出处
《电信科学》
北大核心
2025年第8期197-206,共10页
Telecommunications Science
关键词
算力融合网络
算力下沉
双深度Q网络
资源管理
edge computing fusion network
computing shifting
DDQN
resource management
