智慧医疗场景下考虑终端移动性的任务卸载策略  

Task offloading strategy considering terminal mobility in medical wisdom scenario

作　　者：齐平 束红 QI Ping;SHU Hong(College of Mathematics and Computer Science,Institute of Service Computing,Tongling University,Tongling 244000,China)

机构地区：[1]铜陵学院数学与计算机学院,服务计算研究所,安徽铜陵244000

出　　处：《浙江大学学报（工学版）》2020年第6期1126-1137,共12页Journal of Zhejiang University：Engineering Science

基　　金：国家自然科学基金资助项目(61300042);安徽省高校自然科学基金重点资助项目(KJ2019A0704);铜陵学院校级自然科学重点研究资助项目(2019xyzd06).

摘　　要：针对工作流任务、边缘服务器无线信号覆盖范围、智慧医疗场景以及终端移动路径,分别构建模型进行描述,根据移动终端的实时位置和移动速率构建基于移动路径的工作流任务执行时间及能耗模型.根据边缘服务器的无线通信模型,引入任务执行延迟和任务迁移2种情况以保障服务的连续性和执行时间限制.从全局角度综合考虑任务在云端、边缘服务器和本地的执行效益,设计工作流任务优先级划分算法和边缘服务器卸载优化算法,并使用遗传算法设计基于最佳移动路径的工作流任务卸载决策及调度算法,在可选路径中搜索满足用户响应时间约束,且移动端能耗最低的最佳路径和相应的任务卸载、调度方案.仿真结果说明:该算法能够合理地分配计算资源,在用户响应时间约束下充分降低移动终端能耗,相较未考虑终端移动性的卸载算法,移动端能耗降低了19.8%.The workflow task,coverage of wireless communication,medical wisdom scenario and moving path were modeled,respectively.According to the location and the velocity of the mobile terminal,the execution time and energy consumption model based on the moving path were constructed.Based on the wireless communication model of different edge servers,task deferred execution and task migration were introduced to guarantee the service continuity and execution time constraint.Then,considering the execution benefits of the cloud,the edge server and the mobile terminal from a global viewpoint,the priority segmentation algorithm and the task offloading optimization algorithm were proposed.Meanwhile,the genetic algorithm was used to find the optimal path and solve the energy consumption optimization problem with the constraint of response time.The experimental results show that the proposed algorithm lowered the mobile energy consumption by 19.8%,compared with the offloading algorithm without considering terminal mobility.Thus,the algorithm can effectively reduce the energy consumption of edge device with the constraint of response time.

关 键 词：移动边缘计算 智慧医疗 计算卸载 边缘服务器 遗传算法 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]