异步移动边缘计算网络中的联合任务调度与计算资源分配优化策略  

Joint Task Scheduling and Computing Resource Allocation Optimization Strategy in Asynchronous Mobile Edge Computing Networks

作　　者：王汝言[1,2,3] 杨安琪 吴大鹏 唐桐[1,2,3] 祝志远 WANG Ruyan;YANG Anqi;WU Dapeng;TANG Tong;ZHU Zhiyuan(School of Communications and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China;Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China,Chongqing 400065,China;Chongqing Key Laboratory of Ubiquitous Sensing and Networking,Chongqing 400065,China)

机构地区：[1]重庆邮电大学通信与信息工程学院,重庆400065 [2]先进网络与智能互联技术重庆市高校重点实验室,重庆400065 [3]泛在感知与互联重庆市重点实验室,重庆400065

出　　处：《电子与信息学报》2025年第2期470-479,共10页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(62271096,U20A20157);重庆市自然科学基金(CSTB2023NSCQ-LZX0134);重庆市高校创新研究群体(CXQT20017);重邮信通青创团队支持计划(SCIE-QN-2022-04);重庆市教委科学技术研究项目(KJQN202300632);重庆市博士后特别资助项目(2022CQBSHTB2057);重庆市研究生科研创新项目(CYB22250)。

摘　　要：移动边缘计算(MEC)通过将密集型任务从传感器卸载到附近边缘服务器,来增强本地的计算能力,延长其电池寿命。然而,在面向无线传感器网等时变环境中,任务之间的异构性可能会导致通信低效率、高时延等问题。为此,该文提出一种异步移动边缘计算网络中的联合任务调度与计算资源分配优化策略,该策略实时感知任务信息年龄和能耗,将异步边缘卸载问题数学建模为NP难(NP-hard problem)的混合整数规划问题,并提出基于混合动作优势演员-评论家(HA2C)强化学习算法的任务调度和计算资源分配方案解决该问题。仿真结果表明,该文方法能显著降低异步卸载网络的平均信息年龄和能耗,满足无线传感器网络对任务时效性的要求。Objective Mobile Edge Computing(MEC)is a key technology for addressing the limited computing capabilities and energy constraints of wireless devices.MEC improves local computing performance and extends battery life by offloading computationally intensive tasks from sensors to nearby edge servers.However,in dynamic environments such as anomaly detection,environmental monitoring,and vehicle positioning,task heterogeneity becomes a significant factor limiting performance.For example,the asynchrony of task generation times can result in issues such as low communication efficiency and increased latency.Furthermore,traditional latency measurement techniques often fail to accurately assess task timeliness.To address these challenges,this paper proposes a strategy for the joint optimization of task scheduling and computational resource allocation in asynchronous MEC networks.The proposed strategy adaptively optimizes task scheduling and resource allocation,minimizing the average information age and energy consumption,thereby enhancing overall system performance.Methods This paper focuses on age-aware asynchronous MEC offloading and resource allocation.Specifically,a mathematical model is formulated based on the First Come First Served(FCFS)queuing principle,considering the order of asynchronous task arrivals.This model optimizes task scheduling and computational resource allocation in asynchronous MEC offloading,with the goal of minimizing the Average Age of Information(AoI)and average energy consumption.In dynamic asynchronous MEC,optimization problems are inherently complex.When these tasks involve both binary offloading decisions and continuous resource allocation,the combination of actions further complicates problem-solving,transforming it into a non-convex optimization challenge.Additionally,the actor network of the Actor-Critic algorithm(A2C)adapts its output layer to either a Categorical or Gaussian distribution,depending on whether the action space is discrete or continuous.This paper proposes a Hybrid Advantage

关 键 词：异步移动边缘计算 无线传感器网络 平均信息年龄 平均能耗 混合动作强化学习 

分 类 号：TN929.5[电子电信—通信与信息系统]