移动边缘计算中能耗优化的多重资源计算卸载策略
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摘要
移动边缘计算环境中边缘设备的能耗优化主要采用计算卸载策略。然而目前常用的计算卸载策略大多只考虑单一的计算资源,没有对移动边缘计算环境中不同种类的计算资源进行综合考虑,无法在保证响应时间约束的情况下充分降低边缘设备能耗。为了解决这一问题,在移动边缘计算环境中提出一种多重资源计算卸载能耗模型,设计了一种新的评价边缘设备能耗的适应度计算方法,并结合工作流管理系统提出了移动边缘计算中能耗优化的多重资源计算卸载粒子群任务调度算法,该算法能够在考虑响应时间约束的情况下,充分降低移动终端能耗。实验表明,与已有4种计算卸载策略相比,新策略所对应的任务调度算法收敛稳定、适应度最优,在用户响应时间约束下,任务调度方案的边缘设备能耗值优于其他4种卸载策略。
In the research on energy efficiency optimization of mobile edge computing,the computation offloading strategy of edge device is emphasis.However,the existing computation offloading strategy can only consider single computing resource and do not take into account the different type of computing resources in mobile edge computing,which cannot reduce the energy consumption of edge device with response time constraint.Therefore,a energy model of multi-resources computation offloading was proposed,and the fitness computation method of task scheduling plan was designed to evaluate the energy consumption of edge device.An energy efficient multi-resource computation offloading strategy task scheduling algorithm was presented to solve the energy consumption optimization problem of edge device.Experimental results showed that the propose algorithm could always achieve stable convergence speed,the optimal fitness and low energy consumption of edge device with the constraint of response time.
In the research on energy efficiency optimization of mobile edge computing,the computation offloading strategy of edge device is emphasis.However,the existing computation offloading strategy can only consider single computing resource and do not take into account the different type of computing resources in mobile edge computing,which cannot reduce the energy consumption of edge device with response time constraint.Therefore,a energy model of multi-resources computation offloading was proposed,and the fitness computation method of task scheduling plan was designed to evaluate the energy consumption of edge device.An energy efficient multi-resource computation offloading strategy task scheduling algorithm was presented to solve the energy consumption optimization problem of edge device.Experimental results showed that the propose algorithm could always achieve stable convergence speed,the optimal fitness and low energy consumption of edge device with the constraint of response time.
引文
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[2]HU Y,PATEL M,SABELLA D,et al.Mobile edge computing-a key technology towards 5g[J].ETSI White Paper,2015,11(11):1-16.
[3]LI H,SHOU G,HU Y,et al.Mobile edge computing:progress and challenges[C]//Proceedings of the 4th IEEE International Conference on Mobile Cloud Computing,Services,and Engineering(MobileCloud).Washington,D.C.,USA:IEEE,2016:83-84.
[4]MACH P,BECVAR Z.Mobile edge computing:a survey on architecture and computation offloading[J].IEEE Communications Surveys&Tutorials,2017,19(3):1628-1656.
[5]PHAM T P,DURILLO J,FAHRINGER T.Predicting workflow task execution time in the cloud using a two-stage machine learning approach[J].IEEE Transactions on Cloud Computing,2017:1-13,DOI:10.1109/TCC.2017.2732344.
[6]RIMAL B,MAIER M.Workflow scheduling in multi-tenant cloud computing environments[J].IEEE Transactions on Parallel and Distributed Systems,2017,28(1):290-304.
[7]WEN L,WANG J,AALST W,et al.Mining process models with prime invisible tasks[J].Data&Knowledge Engineering,2010,69(10):999-1021.
[8]ROMAN R,LOPEZ J,MAMBO M.Mobile edge computing,fog et al.:a survey and analysis of security threats and challenges[J].Future Generation Computer Systems,2018,78(1):680-698.
[9]SHI W,DUSTDAR S.The promise of edge computing[J].Computer,2016,49(5):78-81.
[10]BOUET M,CONAN V.Mobile edge computing resources optimization:a GEMO-clustering approach[J].IEEE Transactions on Network and Service Management,2018,15(2):787-796.
[11]SUN X,ANSARI N.EdgeIoT:Mobile edge computing for the Internet of things[J].IEEE Communications Magazine,2016,54(12):22-29.
[12]TANG L,HE S.Multi-user computation offloading in mobile edge computing:a behavioral perspective[J].IEEE Network,2018,32(1):48-53.
[13]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.
[14]LIU J,MAO Y,ZHANG J,et al.Delay-optimal computation task scheduling for mobile-edge computing systems[C]//Proceedings of the IEEE International Symposium on Information ThEMOry(ISIT).Washington,D.C.,USA:IEEE,2016:1451-1455.
[15]MAO Y,ZHANG J,LETAIEF K.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]ZHAO Y,ZHOU S,ZHAO T,et al.Energy-efficient task offloading for multiuser mobile cloud computing[C]//Proceedings of the IEEE/CIC International Conference on Communications in China(ICCC).Washington,D.C.,USA:IEEE,2015:1-5.
[17]YOU C,HUANG K.Multiuser resource allocation for mobile-edge computation offloading[C]//Proceedings of the Global Communications Conference(GLOBECOM).Washington,D.C.,USA:IEEE,2016:1-6.
[18]RAMREZ-GALLEGO S,FERNNDEZ A,GARCA S,et al.Big data:tutorial and guidelines on information and process fusion for analytics algorithms with mapreduce[J].Information Fusion,2018,42(6):51-61.
[19]CAO S,TAO X,HOU Y,et al.An energy-optimal offloading algorithm of mobile computing based on hetnets[C]//Proceedings of the 2015International Conference on Connected Vehicles and Expo(ICCVE).Washington,D.C.,USA:IEEE,2015:254-258.
[20]MAO Y,YOU C,ZHANG J,et al.A survey on mobile edge computing:the communication perspective[J].IEEE Communications Surveys&Tutorials,2017,19(4):2322-2358.
[21]LI Xuejun,XU Jia,WANG Futian,et al.Energy aware task scheduling algorithm in cloud workflow system[J].Pattern Recognition and Artificial Intelligence,2016,29(9):790-796(in Chinese).[李学俊,徐佳,王福田,等.云工作流系统中能耗感知的任务调度算法[J].模式识别与人工智能,2016,29(9):790-796.]
[22]LI Xuejun,XU Jia,ZHU Erzhou,et al.A novel computation method for adaptive inertia weight of task scheduling algorithm[J].Journal of Computer Research and Development,2016,53(9):1990-1999(in Chinese).[李学俊,徐佳,朱二周,等.任务调度算法中新的自适应惯性权重计算方法[J].计算机研究与发展,2016,53(9):1990-1999.]
[23]SARANGI S,GOEL S,SINGH B.Energy efficient scheduling in iot networks[C]//Proceedings of the SAC:Symposium on Applied Computing.New York,N.Y.,USA:ACM,2018:1-8.