服务缓存约束下优化用户设备执行成本的任务卸载策略  

Cost-minimizing Task Offload Strategy for Mobile Devices Under Service Cache Constraint

作　　者：张俊娜[1,2] 陈家伟[1] 鲍想 刘春红 袁培燕[1] ZHANG Junna;CHEN Jiawei;BAO Xiang;LIU Chunhong;YUAN Peiyan(School of Computer and Information Engineering,Henan Normal University,Xinxiang,Henan 453007,China;Engineering Lab of Intelligence Business&Internet of Things,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 [2]河南师范大学智慧商务与物联网技术河南省工程实验室,河南新乡453007

出　　处：《计算机科学》2023年第10期275-281,共7页Computer Science

基　　金：国家自然科学基金(61902112,62072159);广西密码学与信息安全重点实验室课题(GCIS202115)。

摘　　要：边缘计算通过在网络边缘侧提供更优的计算和存储能力,能够有效降低用户设备的执行时延和能耗。随着应用程序对计算和存储资源的需求越来越大,任务卸载作为消除用户设备固有限制的一种有效手段,成为了主要的研究热点之一。然而,在已有的任务卸载研究中,常常忽略不同类型的任务对服务需求的多样性以及边缘服务器服务缓存有限的情形,从而导致不可行的卸载决策。因此,在服务缓存约束下,研究了能够使得用户设备执行成本最优的任务卸载问题。首先设计了云服务器、边缘服务器和本地设备的协同卸载模型,用于平衡边缘服务器的负载问题,同时借助云服务器弥补边缘服务器有限的服务缓存能力。然后,提出了适用于云边端协同的任务卸载算法,优化用户设备的执行成本。当任务被卸载时,先采用改进的贪婪算法选择最佳的边缘服务器,再通过比较任务在不同位置上的执行成本,来确定任务的卸载决策。实验结果表明,所提算法相比对比算法能够有效降低用户设备的执行成本。Edge computing provides more computing and storage capabilities at the edge of the network to effectively reduce execution delay and power consumption of mobile devices.Since applications consume more and more computing and storage resources,task offloading has become one of effective solutions to address the inherent limitations in mobile terminals.However,existing researches on task offloading often ignore the diversity of service requirements for different types of tasks and that edge servers have limited services capabilities,resulting in infeasible offloading decisions.Therefore,we study the task offloading problem that can optimize the execution cost of mobile devices under service cache constraints.We first design a collaborative offloading model integrated remote cloud,edge server and local device to balance the load of edge server.Meanwhile,cloud server is used to make up for the limited-service caching capacity of the edge server.Secondly,a task offloading algorithm suitable for cloud-edge-device collaboration is proposed to optimize the execution delay and energy cost of mobile devices.When the task is offloaded,the improved greedy algorithm is used to select the best edge server.Then,the offload decision of the task is determined by comparing the execution cost of the task at different locations.Experimental results show that the proposed algorithm can effectively reduce the execution cost of mobile devices compared with the comparison algorithms.

关 键 词：边缘计算 任务卸载 云边端协同 服务缓存 卸载策略优化 

分 类 号：TP302[自动化与计算机技术—计算机系统结构]