基于动态服务缓存辅助的任务卸载方法  

Task offloading method based on dynamic service cache assistance

作　　者：张俊娜[1] 王欣新 李天泽 赵晓焱[1] 袁培燕[1] ZHANG Junna;WANG Xinxin;LI Tianze;ZHAO Xiaoyan;YUAN Peiyan(College of Computer and Information Engineering,Henan Normal University,Xinxiang Henan 453007,China)

机构地区：[1]河南师范大学计算机与信息工程学院,河南新乡453007

出　　处：《计算机应用》2024年第5期1493-1500,共8页journal of Computer Applications

基　　金：国家自然科学基金资助项目(62072159);河南省科技攻关项目(232102211061,222102210011)。

摘　　要：针对服务缓存和任务卸载联合优化中,由于缺乏对用户服务请求多样性和动态性的综合考虑而导致的用户体验质量降低问题,提出一种基于动态服务缓存辅助的任务卸载方法。首先,针对边缘服务器执行缓存服务动作空间较大的问题,重新定义了动作,并筛选出最优的动作集合以提高算法训练的效率;其次,设计一种改进的多智能体Q-Learning算法学习最优的服务缓存策略;再次,将任务卸载问题转换为凸优化问题,利用凸优化工具获得最优解;最后,利用拉格朗日对偶法求得最优的计算资源分配策略。为了验证所提方法的有效性,基于真实数据集进行了充分的实验。实验结果表明,对比Q-Learning、双层深度Q网络(D2QN)以及多智能体深度确定性策略梯度(MADDPG)方法,所提方法的响应时间分别降低了8.5%、11.8%和12.6%,平均体验质量分别提高了1.5%、2.7%和4.3%。Aiming at the problem of user experience quality degradation due to the lack of comprehensive consideration of the diversity and dynamics of user service requests in the joint optimization of service caching and task offloading,a task offloading method based on dynamic service cache assistance was proposed.Firstly,to address the problem of the large action spaces for edge servers performing caching service,the actions were redefined and the optimal set of actions was selected to improve the efficiency of algorithm training.Secondly,an improved multi-agent Q-Learning algorithm was designed to learn an optimal service caching policy.Thirdly,the task offloading problem was converted into a convex optimization problem,and the optimal solution was obtained using a convex optimization tool.Finally,the optimal computational resource allocation policy was found using the Lagrangian dual method.To verify the effectiveness of the proposed method,extensive experiments were conducted based on a real dataset.Experimental results show that the response time of the proposed method is reduced by 8.5%,11.8%and 12.6%,respectively,and the average quality of experience is improved by 1.5%,2.7%and 4.3%,respectively,compared with Q-Learning,Double Deep Q Network(D2QN)and Multi-Agent Deep Deterministic Policy Gradient(MADDPG)method.

关 键 词：边缘计算 动态服务缓存 任务卸载 计算资源分配 服务多样性 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]