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浙江大学学报(工学版)
浙江大学学报(工学版)  2020, Vol. 54 Issue (6): 1126-1137    DOI: 10.3785/j.issn.1008-973X.2020.06.009
计算机技术     
智慧医疗场景下考虑终端移动性的任务卸载策略
齐平(),束红
铜陵学院 数学与计算机学院，服务计算研究所，安徽 铜陵 244000
Task offloading strategy considering terminal mobility in medical wisdom scenario
Ping QI(),Hong SHU
College of Mathematics and Computer Science, Institute of Service Computing, Tongling University, Tongling 244000, China
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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%.
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    
Abstract:

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.
Key words: mobile edge computing    medical wisdom    computation offloading    edge server    genetic algorithm
收稿日期: 2019-12-09 出版日期: 2020-07-06
CLC:  TP 393  
基金资助: 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）
作者简介: 齐平（1981—），男，副教授，博士，从事可信计算、移动边缘计算研究. orcid.org/0000-0001-7305-7911. E-mail： qiping929@gmail.com
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引用本文:

齐平,束红. 智慧医疗场景下考虑终端移动性的任务卸载策略[J]. 浙江大学学报(工学版), 2020, 54(6): 1126-1137.

Ping QI,Hong SHU. Task offloading strategy considering terminal mobility in medical wisdom scenario. Journal of ZheJiang University (Engineering Science), 2020, 54(6): 1126-1137.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2020.06.009    或    http://www.zjujournals.com/eng/CN/Y2020/V54/I6/1126

图 1  移动边缘计算（MEC）环境下的4种计算卸载模型
图 2  移动边缘环境下智慧医院及移动路径剖面图
图 3  移动边缘环境下智慧医院及移动路径平面图
图 4  工作流实例及抽象工作流有向无环（DAG）图
图 5  边缘服务器能耗模型
图 6  移动终端数据发送过程
图 7  任务卸载失败情形示意图
图 8  任务延迟执行及任务迁移执行
图 9  不同移动路径下的任务执行能耗比较
图 10  不同边缘服务器数量情况下的任务执行能耗比较
图 11  不同任务数情况下WTOSSABOP、CLOUD、EDGE、MOBILE和LoPRTC的任务执行能耗比较
图 12  不同任务数情况下WTOSSABOP、CLOUD、EDGE、MOBILE和LoPRTC的任务完工时间比较
图 13  不同任务类型情况下WTOSSABOP、CLOUD、EDGE、MOBILE和LoPRTC的任务执行能耗比较
图 14  不同任务类型情况下WTOSSABOP、CLOUD、EDGE、MOBILE和LoPRTC的任务完工时间比较
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