数字孪生边缘网络端到端时延优化的任务卸载与资源分配方法  

Task Offloading and Resource Allocation Method for End-to-End Delay Optimization in Digital Twin Edge Networks

作　　者：李松 李顺 王博文 孙彦景 LI Song;LI Shun;WANG Bowen;SUN Yanjing(School of Information and Control Engineering,China University of Mining and Technology,Xuzhou 221116,China;Xuzhou Engineering Research Center of Intelligent Industry Safety and Emergency Collaboration,Xuzhou 221116,China)

机构地区：[1]中国矿业大学信息与控制工程学院,徐州221116 [2]徐州市智能安全与应急协同工程研究中心,徐州221116

出　　处：《电子与信息学报》2025年第3期633-644,共12页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(62071472,62101556);江苏省自然科学基金(BK20210489);中央高校基本科研业务费项目(2020ZDPYMS26);江苏省研究生科研与实践创新计划项目(KYCX24_2763);中国矿业大学研究生创新计划项目(2024WLJCRCZL133);西安市网络融合通信重点实验室开放基金项目(2022NCC-N103);海南省省属科研院所技术创新项目(KYYSGY2024-005);工信部项目(CBG01N23-01-04)。

摘　　要：针对移动边缘计算(MEC)场景中任务卸载、计算和结果反馈全过程时延优化问题,该文提出了一种数字孪生(DT)辅助的联合MEC任务卸载、设备关联与资源分配的端到端时延优化方法。首先,在数字孪生边缘网络(DITEN)框架下,为包含传感器、边缘服务器以及执行器构成的边缘计算网络建立了物理模型与数字孪生模型,以及全过程边缘网络任务模型并推导了任务端到端时延,进而建立了时延、能耗等约束下的端到端时延优化问题。其次,为解决所提出的混合整数非凸优化问题,将原问题分解为4个子问题,并提出了一种基于内部凸近似方法和匈牙利算法的交替优化算法。在DT辅助下联合优化了设备关联、卸载比例、发射功率、传输带宽以及DT估计处理速率。最后,仿真结果表明,与其他基准方案相比,所提联合优化方案显著降低了端到端时延。Objective The rapid development of wireless communication and the Internet of Things(IoT)has led to significant growth in compute-intensive and delay-sensitive applications,which impose stricter latency requirements.However,local devices often face challenges in meeting these demands due to limitations in storage,computing power,and battery life.Mobile Edge Computing(MEC)has emerged as a key technology to address these issues.Despite its potential,the dynamic and complex nature of edge networks presents significant challenges in task offloading and resource allocation.DIgital Twin Edge Networks(DITEN),which map digital twins to physical devices in real-time,offer a promising solution.By integrating MEC with Digital Twin(DT)technology,this approach not only alleviates resource limitations in devices but also optimizes resource allocation in the digital domain,minimizing physical resource waste.This paper tackles the End-to-End(E2E)optimization problem in the offloading,computation,and result feedback process within edge computing networks.A DT-assisted joint task offloading,device association,and resource allocation scheme is proposed for E2E delay optimization,providing theoretical support for improving resource utilization in edge networks.Methods The optimization problem in this paper involves a non-convex objective function with both binary and continuous constraints,making it a mixed integer non-convex problem.To address this,the original problem is decomposed into four subproblems:computation and communication resource optimization,device association optimization,offloading decision optimization,and transmission bandwidth optimization.Within the Alternating Optimization(AO)framework,the Internal Convex Approximation(ICA)method is applied to convert the non-convex problem into a convex one.Additionally,the many-to-one matching problem is transformed into a one-to-one matching problem,and the Hungarian Algorithm(HA)is employed to solve the device association subproblem.Finally,the ICA-HA-AO is proposed to addr

关 键 词：移动边缘计算 数字孪生 任务卸载 资源分配 交替优化 

分 类 号：TN929.5[电子电信—通信与信息系统]