面向时空差异化需求的任务卸载和服务缓存联合优化  

Joint Optimization of Task Offloading and Service Caching for Spatiotemporal Differentiated Requirements

作　　者：梁吉申 张冬雪 余刚 王碧 单鸿久 LIANG Jishen;ZHANG Dongxue;YU Gang;WANG Bi;SHAN Hongjiu(Communications NCO Academy,Army Engineering University of PLA,Chongqing 400035;School of Electronic Information,Chongqing Institute of Engineering,Chongqing 400056;School of Communications and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065)

机构地区：[1]陆军工程大学通信士官学校,重庆400035 [2]重庆工程学院电子信息学院,重庆400056 [3]重庆邮电大学通信与信息工程学院,重庆400065

出　　处：《陆军工程大学学报》2025年第1期10-19,共10页Journal of Army Engineering University of PLA

基　　金：重庆市自然科学基金创新发展联合基金(CSTB2022NSCQ-LZX0055)。

摘　　要：移动边缘计算(mobile edge computing,MEC)可有效解决移动用户业务需求增多、业务处理难度增大的问题。然而,用户业务需求存在时空上的差异化,同时网络环境的深度不确定性等网络特征也制约了网络边缘任务处理和服务缓存效率。针对以上挑战,对多用户多边缘服务器的MEC系统中用户业务卸载和服务缓存问题进行了研究,针对用户在时空上的差异化业务需求特征,以最小化基站下所有用户的平均时延和能耗成本为优化目标,建立基于面向时空差异化需求的任务卸载和服务缓存联合优化。同时,考虑到深度不确定性的网络环境特征,进一步提出了基于图注意力网络和长短期记忆网络的多智能体深度强化学习算法,用于自主学习和决策用户任务卸载、资源分配及服务缓存策略。仿真验证了所提算法具有良好的收敛性和能效。Mobile edge computing(MEC)can effectively address the issues of increasing business demands and greater processing difficulties for mobile users.However,users'service demands have differentiated characteristics in time and space.At the same time,network characteristics such as deep uncertainty in the network environment also restrict the efficiency of network edge task processing and service caching.In response to these challenges,this paper studies the users'service offloading and service caching issues in a multi-user and multi-edge server MEC system.To address the differentiated service requirements of users in both time and space,with the optimization goal of minimizing the average delay and energy consumption cost for all users under the base station,a joint optimization of task offloading and service caching based on the differentiated needs in time and space is established.Taking into consideration the characteristics of the network environment with deep uncertainty,a multi-agent deep reinforcement learning algorithm based on graph neural networks and long short-term memory networks is further proposed for autonomous learning and decision-making of user task offloading,resource allocation,and service caching strategies.Finally,simulations have verified that the proposed algorithm has good convergence and energy efficiency.

关 键 词：边缘计算 业务差异化 任务卸载 资源分配 服务缓存 

分 类 号：TN929.5[电子电信—通信与信息系统]