移动边缘环境下面向工作流管理的计算迁移方法
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摘要
针对移动边缘环境下移动设备大量的能源消耗问题,为了优化移动设备的能源消耗,提出一种能耗感知的工作流计算迁移(EOW)方法。首先,基于排队论分析边缘设备中计算任务的平均等待时间,建立了移动设备的时间模型和能耗模型;然后,基于非支配排序算法(NSGA-Ⅲ)提出对应的计算迁移方法,对工作流的计算任务进行合理的分配,将一部分计算任务留在移动设备处理,或者迁移到边缘计算平台和远程云端,实现每个移动设备的节能目标;最后,通过CloudSim仿真平台对提出的计算迁移方法进行仿真和对比实验。实验结果表明,EOW方法能够明显地减少每个移动设备的能源消耗,同时满足每一个工作流的截止时间的要求。
The problem of high energy consumption for mobile devices in mobile edge computing is becoming increasingly prominent. In order to reduce the energy consumption of the mobile devices, an Energy-aware computation Offloading for Workflows(EOW) was proposed. Technically, the average waiting time of computing tasks in edge devices was analyzed based on queuing theory, and the time consumption and energy consumption models for mobile devices were established. Then a corresponding computation offloading method, by leveraging NSGA-Ⅲ(Non-dominated Sorting Genetic Algorithm Ⅲ) was designed to offload the computing tasks reasonably. Part computing tasks were processed by the mobile devices, or offloaded to the edge computing platform and the remote cloud, achieving the goal of energy-saving for all the mobile devices. Finally, comparison experiments were conducted on the CloudSim platform. The experimental results show that EOW can effectively reduce the energy consumption of all the mobile devices and satisfy the deadline of all the workflows.
The problem of high energy consumption for mobile devices in mobile edge computing is becoming increasingly prominent. In order to reduce the energy consumption of the mobile devices, an Energy-aware computation Offloading for Workflows(EOW) was proposed. Technically, the average waiting time of computing tasks in edge devices was analyzed based on queuing theory, and the time consumption and energy consumption models for mobile devices were established. Then a corresponding computation offloading method, by leveraging NSGA-Ⅲ(Non-dominated Sorting Genetic Algorithm Ⅲ) was designed to offload the computing tasks reasonably. Part computing tasks were processed by the mobile devices, or offloaded to the edge computing platform and the remote cloud, achieving the goal of energy-saving for all the mobile devices. Finally, comparison experiments were conducted on the CloudSim platform. The experimental results show that EOW can effectively reduce the energy consumption of all the mobile devices and satisfy the deadline of all the workflows.
引文
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[2] 崔勇,宋健,缪葱葱,等.移动云计算研究进展与趋势[J].计算机学报,2017,40(2):273-295.(CUI Y,SONG J,MIAO C C,et al.Mobile cloud computing research progress and trends [J].Chinese Journal of Computers,2017,40(2):273-295.)
[3] BARBERA M V,KOSTA S,MEI A,et al.To offload or not to offload?The bandwidth and energy costs of mobile cloud computing [C]// Proceedings of the 2013 IEEE International Conference on Computer Communications.Piscataway,NJ:IEEE,2013:1285-1293.
[4] SHI W,CAO J,ZHANG Q,et al.Edge computing:vision and challenges [J].IEEE Internet of Things Journal,2016,3(5):637-646.
[5] DENG S,HUANG L,TAHERI J,et al.Computation offloading for service workflow in mobile cloud computing [J].IEEE Transactions on Parallel & Distributed Systems,2015,26(12):3317-3329.
[6] XU X,DOU W,ZHANG X,et al.EnReal:An energy-aware resource allocation method for scientific workflow executions in cloud environment [J].IEEE Transactions on Cloud Computing,2016,4(2):166-179.
[7] 赵梓铭,刘芳,蔡志平,等.边缘计算:平台、应用与挑战[J].计算机研究与发展,2018,55(2):327-337.(ZHAN Z M,LIU F,CAI Z P,et al.Edge computing:platforms,applications and challenges [J].Journal of Computer Research and Development,2018,55(2):327-337.)
[8] JIA M,LIANG W,XU Z,et al.Cloudlet load balancing in wireless metropolitan area networks[C]// Proceedings of the 35th Annual IEEE International Conference on Computer Communications.Piscataway,NJ:IEEE,2016:1-9.
[9] 曾广周,党妍.基于移动计算范型的迁移工作流研究[J].计算机学报,2003,26(10):1343-1349.(ZENG G Z,DANG Y.The study of migrating workflow based on the mobile computing paradigm[J].Chinese Journal of Computers,2003,26(10):1343-1349.)
[10] ZHU Z,ZHANG G,LI M,et al.Evolutionary multi-objective workflow scheduling in cloud [J].IEEE Transactions on Parallel and Distributed Systems,2016,27(5):1344-1357.
[11] RIMAL B P,van PHAM D,MAIER M.Cloudlet enhanced fiber-wireless access networks for mobile-edge computing[J].IEEE Transactions on Wireless Communications,2017,16(6):3601-3618.
[12] ELGAZZAR K,MARTIN P,HASSANEIN H.Cloud-assisted computation offloading to support mobile services[J].IEEE Transactions on Cloud Computing,2016,4(3):279-292.
[13] SHI C,HABAK K,PANDURANGAN P,et al.COSMOS:computation offloading as a service for mobile devices[C]// Proceedings of the 15th ACM International Symposium on Mobile Ad Hoc Networking and Computing.New York:ACM,2014:287-296.
[14] LIU Z,ZENG X,HUANG W,et al.Framework for context-aware computation offloading in mobile cloud computing[C]// Proceedings of the 15th IEEE International Symposium on Parallel and Distributed Computing.Piscataway,NJ:IEEE,2016:172-177.
[15] MAO Y,ZHANG J,LETAIEF K B.Dynamic computation offloading for mobile-edge computing with energy harvesting devices[J].IEEE Journal on Selected Areas in Communications,2016,34(12):3590-3605.
[16] ZHANG K,MAO Y,LENG S,et al.Energy-efficient offloading for mobile edge computing in 5G heterogeneous networks[J].IEEE Access,2016,4:5896-5907.
[17] CUI Y,SONG J,REN K,et al.Software defined cooperative offloading for mobile cloudlets[J].IEEE/ACM Transactions on Networking,2017,25(3):1746-1760.
[18] WANG F,XU J,WANG X,et al.Joint offloading and computing optimization in wireless powered mobile-edge computing systems [J].IEEE Transactions on Wireless Communications,2018,17(3):1784-1797.
[19] KUMAR K,LU Y H.Cloud computing for mobile users:can offloading computation save energy?[J].Computer,2010,43(4):51-56.
[20] YANG Z,NIYATO D,PING W.Offloading in mobile cloudlet systems with intermittent connectivity [J].IEEE Transactions on Mobile Computing,2015,14(12):2516-2529.