基于多代理模仿学习的普适边缘计算资源分配  

Resource allocation for pervasive edge computing based on multi-agent imitation learning

作　　者：刘建华 李炜 刘佳嘉 涂晓光 谢家雨[1] LIU Jianhua;LI Wei;LIU Jiajia;TU Xiaoguang;XIE Jiayu(Institute of Electronic and Electrical Engineering,Civil Aviation Flight University of China,Guanghan 618307,China)

机构地区：[1]中国民用航空飞行学院航空电子电气学院,广汉618307

出　　处：《南京信息工程大学学报（自然科学版）》2024年第1期83-96,共14页Journal of Nanjing University of Information Science & Technology（Natural Science Edition）

基　　金：四川省科技厅科普创作项目(2022J DKP0093);四川省科技创新苗子工程重点项目(2022JDRC0076);中央高校基本科研业务费专项基金(ZHMH2022-004,J2022-025)。

摘　　要：普适边缘计算允许对等设备之间建立独立通信连接,能帮助用户以较低的时延处理海量的计算任务.然而,分散的设备中不能实时获取到网络的全局系统状态,无法保证设备资源利用的公平性.针对该问题,提出了一种基于生成对抗网络(Generative Adversarial Network, GAN)的普适边缘计算资源分配方案.首先基于最小化时延与能耗建立多目标优化问题,然后根据随机博弈理论将优化问题转化为最大奖励问题,接着提出一种基于多代理模仿学习的计算卸载算法,该算法将多代理生成对抗模仿学习(GAIL)和马尔可夫策略(Markov Decision Process, MDP)相结合以逼近专家性能,实现了算法的在线执行,最后结合非支配排序遗传算法Ⅱ(Non-dominated Sorting Genetic AlgorithmⅡ,NSGA-Ⅱ)对时延和能耗进行了联合优化.仿真结果表明,所提出的解决方案与其他边缘计算资源分配方案相比,时延缩短了30.8%,能耗降低了34.3%.Pervasive edge computing allows peer devices to establish independent communication connections,which enables users to process massive computing tasks with low delay.However,distributed devices cannot obtain the global system status of the network in real time,thus the fairness of resource utilization cannot be guaranteed.To solve this problem,a resource allocation scheme for pervasive edge computing based on Generative Adversarial Network(GAN)is proposed.In this scheme,a multi-objective optimization problem is established for minimizing the time delay and energy consumption,which is then transformed into a maximum reward problem according to the random game theory.And then a computation offloading algorithm based on multi-agent imitation learning is proposed,which combines multi-agent Generative Adversarial Imitation Learning(GAIL)and Markov Decision Process(MDP)to approximate the performance of experts,and realizes online execution of the algorithm.Finally,combined with Non-dominated Sorting Genetic AlgorithmⅡ(NSGA-Ⅱ),the time delay and energy consumption are jointly optimized.Simulation results show that,compared with other edge computing resource allocation schemes,the proposed solution shortened the time delay by 30.8%and reduced the energy consumption by 34.3%.

关 键 词：边缘计算 模仿学习 分布式计算 联合优化 资源分配 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]