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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2020, Vol. 54 Issue (6): 1126-1137    DOI: 10.3785/j.issn.1008-973X.2020.06.009
Computer Technology     
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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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     
Received: 09 December 2019      Published: 06 July 2020
CLC:  TP 393  
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Ping QI
Hong SHU
Cite this article:

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.

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


智慧医疗场景下考虑终端移动性的任务卸载策略

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 
Fig.1 Four kinds of computation offloading models in mobile edge computing（MEC）environment
Fig.2 Cross-sectional view of wisdom hospital and moving path in MEC environment
Fig.3 Planar graph of wisdom hospital and moving path in MEC environment
Fig.4 Workflow instance and abstract workflow directed acyclic （DAG）graph
Fig.5 Energy consumption model of edge servers
Fig.6 Data transmitting procedure of mobile terminal
Fig.7 Schematic of situation of task offloading failure
Fig.8 Task delay and task migration execution
Fig.9 Comparison of task execution energy consumption under different moving paths
Fig.10 Comparison of task energy consumption with different numbers of edge servers
Fig.11 Comparison of task energy consumption of WTOSSABOP，CLOUD，EDGE，MOBILE and LoPRTC with different numbers of tasks
Fig.12 Comparison of task execution time of WTOSSABOP，CLOUD，EDGE，MOBILE and LoPRTC with different numbers of tasks
Fig.13 Comparison of task energy consumption of WTOSSABOP，CLOUD，EDGE，MOBILE and LoPRTC with different types of tasks
Fig.14 Comparison of task execution time of WTOSSABOP，CLOUD，EDGE，MOBILE and LoPRTC with different types of tasks
[1]   ABBAS N, ZHANG Y, TAHERKORDI A, et al Mobile edge computing: a survey[J]. IEEE Internet of Things Journal, 2017, 5 (1): 450- 465
[2]   施巍松, 孙辉, 曹杰, 等 边缘计算: 万物互联时代新型计算模型[J]. 计算机研究与发展, 2017, 54 (5): 907- 924
SHI Wei-song, SUN Hui, CAO Jie, et al Edge computing: an emerging model for the internet ofeverything era[J]. Journal of Computer Research and Development, 2017, 54 (5): 907- 924
doi: 10.7544/issn1000-1239.2017.20160941
[3]   施巍松, 张星洲, 王一帆, 等 边缘计算: 现状与展望[J]. 计算机研究与发展, 2019, 56 (01): 73- 93
SHI Wei-song, ZHANG Xing-zhou, WANG Yi-fan, et al Edge computing: state-of-the-art and future directions[J]. Journal of Computer Research and Development, 2019, 56 (01): 73- 93
[4]   倪明选, 张黔, 谭浩宇, 等 智慧医疗——从物联网到云计算[J]. 中国科学: 信息科学, 2013, 43 (4): 515- 528
NI Ming-xuan, ZHANG Qian, TAN Hao-yu, et al Smart healthcare: from IoT to cloud computing[J]. SCIENTIA SINICA Informationis, 2013, 43 (4): 515- 528
doi: 10.1360/112012-616
[5]   邱宇, 王持, 齐开悦, 等 智慧健康研究综述: 从云端到边缘的系统[J]. 计算机研究与发展, 2019, 57 (1): 53- 73
QIU Yu, WANG Chi, QI Kai-yue, et al A survey of smart health: system design from the cloud to the edge[J]. Journal of Computer Research and Development, 2019, 57 (1): 53- 73
[6]   MIN C, MAO S, LIU Y Big data: a survey[J]. Mobile Networks and Applications, 2014, 19 (2): 171- 209
doi: 10.1007/s11036-013-0489-0
[7]   谢人超, 廉晓飞, 贾庆, 等 移动边缘计算卸载技术综述[J]. 通信学报, 2018, 39 (11): 142- 159
XIE Ren-chao, LIAN Xiao-fei, JIA Qin, et al Survey on computation offloading in mobile edge computing[J]. Journal on Communications, 2018, 39 (11): 142- 159
[8]   CHAMLA V, THAM C K, CHALAPATHI G S S. Latency aware mobile task assignment and load balancing for edge cloudlets [C] // 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). Kona: IEEE, 2017: 587-592.
[9]   KUANG Z K, GUO S T, LIU J D, et al A quick-response framework for multi-user computation offloading in mobile cloud computing[J]. Future Generation Computer Systems, 2018, 81 (4): 166- 176
[10]   ZHANG J, HU X, NING Z, et al Energy-latency tradeoff for energy-aware offloading in mobile edge computing networks[J]. IEEE Internet of Things Journal, 2018, 5 (4): 2633
doi: 10.1109/JIOT.2017.2786343
[11]   WANG X, LUO D Energy efficiency based resource schedule in mobile cloud computing[J]. Journal of Computational and Theoretical Nanoscience, 2015, 12 (2): 239- 243
doi: 10.1166/jctn.2015.3724
[12]   袁友伟, 刘恒初, 俞东进, 等 面向边缘侧卸载优化的工作流动态关键路径调度算法[J]. 计算机集成制造系统, 2019, 25 (4): 798- 808
YUAN You-wei, LIU Heng-chu, YU Dong-jin, et al Offloading optimization base on dynamic critical path in mobile edge computing environment[J]. Computer Integrated Manufacturing Systems, 2019, 25 (4): 798- 808
[13]   徐佳, 李学俊, 丁瑞苗, 等 移动边缘计算中能耗优化的多重资源计算卸载策略[J]. 计算机集成制造系统, 2019, 25 (4): 954- 961
XU J, LI Xue-jun, DING Rui-miao, et al Energy efficient multi-resource computation offloading strategy in mobile edge computing[J]. Computer Integrated Manufacturing Systems, 2019, 25 (4): 954- 961
[14]   NAN Y, LI W, BAO W, et al Adaptive energy-aware computation offloading for cloud of things systems[J]. IEEE Access, 2017, 5: 23947- 23957
doi: 10.1109/ACCESS.2017.2766165
[15]   WANG W, ZHOU W. Computational offloading with delay and capacity constraints in mobile edge [C] // 2017 IEEE International Conference on Communications. Paris: IEEE, 2017: 1-6.
[16]   CHEN M, QIAN Y F, CHEN J, et al Privacy protection and intrusion avoidance for cloudlet-based medical data sharing[J]. IEEE Transactions on Cloud Computing, 2016, 2 (4): 2529- 2533
[17]   ZHAO H L, DENG S G, ZHANG C, et al. A mobility-aware cross-edge computation offloading framework for partitionable applications [C] // 2019 The International Conference on Web Services(ICWS). Milan: IEEE, 2019: 193-200.
[18]   NADEMBEGA A, HAFID A S, BRISEBOIS R. Mobility prediction model-based service migration procedure for follow me cloud to support QoS and QoE [C] // 2016 IEEE International Conference on Communications. Kuala Lumpur: IEEE, 2016: 1-6.
[19]   WANG S, URGAONKAR R, HE T, et al Dynamic service placement for mobile micro-clouds with predicted future costs[J]. IEEE Transactions on Parallel and Distributed Systems, 2017, 28 (4): 1002- 1016
doi: 10.1109/TPDS.2016.2604814
[20]   ZHU T, SHI T, LI J, et al Task scheduling in deadline-aware mobile edge computing systems[J]. IEEE Internet of Things Journal, 2019, 6 (3): 4854- 4866
doi: 10.1109/JIOT.2018.2874954
[21]   KOMNIOS I, TSAPELI F, GORINSKY S Cost-effective multi-mode offloading with peer-assisted communications[J]. Ad Hoc Networks, 2015, 25: 370- 382
doi: 10.1016/j.adhoc.2014.07.028
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