移动云环境中数据流应用的Cloudlet选择策略研究
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摘要
现有的Cloudlet选择策略大多只使用单个Cloudlet资源进行计算卸载,对于拥有较多可并行执行组件的移动数据流应用程序,性能提升有限。针对这一问题,提出一种基于化学反应优化算法的Cloudlet选择策略。该策略以减少应用的完成时间和移动设备能耗为目的,在满足应用程序组件间依赖关系的前提下,充分利用多Cloudlet的计算资源使移动数据流应用的并行组件同时执行,提升了应用执行效率的同时降低了移动设备能耗。仿真实验表明,在多Cloudlet环境中应用程序的性能相较于POCSS策略平均提升了18.2%。
Most existing Cloudlet selection strategies only used the resources of one Cloudlet to compute offloading,which couldn't obtain the superior performance improvement for mobile data streaming application with many parallel components.To address this issue,a Cloudlet selection strategy based on chemical reaction optimization algorithm was proposed.The strategy aims to reduce application's completion time and energy consumption of mobile device.When the dependencies among application's components was satisfied,the strategy can take full advantage of the computing capability of multi-cloudlet to execute the parallel components of mobile data stream application simultaneously.Therefore the strategy can improve the execution efficiency and reduce the energy consumption of mobile device.The simulation results reveal that the proposed strategy can achieves 18.3% on average performance improvement than POCSS strategy does in multi-Cloudlet environment.
Most existing Cloudlet selection strategies only used the resources of one Cloudlet to compute offloading,which couldn't obtain the superior performance improvement for mobile data streaming application with many parallel components.To address this issue,a Cloudlet selection strategy based on chemical reaction optimization algorithm was proposed.The strategy aims to reduce application's completion time and energy consumption of mobile device.When the dependencies among application's components was satisfied,the strategy can take full advantage of the computing capability of multi-cloudlet to execute the parallel components of mobile data stream application simultaneously.Therefore the strategy can improve the execution efficiency and reduce the energy consumption of mobile device.The simulation results reveal that the proposed strategy can achieves 18.3% on average performance improvement than POCSS strategy does in multi-Cloudlet environment.
引文
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[2]SHAHZAD M,LIU A X,SAMUEL A. Secure unlocking of mobile touch screen devices by simple gestures:you can see it but you can not do it[C]//International Conference on Mobile Computing&NETWORKING. 2013:39-50.
[3]AKARIMAN Q,JATI A N, NOVIANTY A. Face recognition based on the Android device using LBP algorithm[C]//International Conference on Control, Electronics, Renewable Energy and Communications.2015:166-170.
[4]曹洋,江涛,杨世永,等.移动云计算网络中的最优资源分配研究[J].通信学报,2011,32(9A):42-48.CAO Y, JIANG T, YANG S Y, et al. Optimal resource allocation in mobile cloud computing network[J]. Journal on Communications,2011,32(9A):42-48.
[5]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.
[6]CUERVO E, BALASUBRAMANLAN A, Cho D, et al. MAUI:making smartphones last longer with code offload[C]//Proceedings of the8th international conference on Mobile systems, applications, and services. 2010:49-62.
[7]CHUN B G, IHM S, MANIATIS P, et al. Clonecloud:elastic execution between mobile device and cloud[C]//Proceedings of the sixth conference on Computer systems. 2011:301-314.
[8]RA M R, SHETH A, Mummert L, et al. Odessa:enabling interactiveperception applications on mobile devices[C]//Proceedings of the 9th international conference on Mobile systems, applications, and services.2011:43-56.
[9]张文丽,郭兵,沈艳,等.智能移动终端计算迁移研究[J].计算机学报,2016, 39(5):1021-1038.ZHANG W L, GUO B, SHEN Y, et al. Computation offloading on intelligent mobile terminal[J]. Chinese Journal of Computers, 2016,39(5):1021-1038.
[10]SATYANARAYANAN M, BAHL P, CACERES R, et al. The case for VM-based Cloudlets in mobile computing[J]. IEEE Pervasive Computing, 2009, 8(4):14-23.
[11]华夏进,董瑞志,彭鑫,等.基于统计预测的Cloudlet调度机制研究[J].小型微型计算机系统,2016, 37(3):406-411.HUA X J, DONG R Z, PENG X, et al. Cloudlet scheduling mechanism research based on the statistical forecasting[J]. Journal of Chinese Mini-Micro Computer Systems, 2016, 37(3):406-411.
[12]YANG L, CAO J, TANG S, et al. Run time application repartitioning in dynamic mobile cloud environments[J]. IEEE Transactions on Cloud Computing, 2016,4(3):336-348.
[13]PILLAI P S, MUMMERT L B, SCHLOSSER S W, et al. SLIPstream:scalable low-latency interactive perception on streaming data[C]//International Workshop on Network and Operating Systems Support for Digital Audio and Video. 2009:43-48.
[14]MUKHERJEE A, DE D, ROY D. A power and latency aware Cloudlet selection strategy for multi-Cloudlet environment[J]. IEEE Transactions on Cloud Computing, 2016:1.
[15]PARMAR D, KUMAR A S, NIVANGUNE A, et al. Discovery and selection mechanism of Cloudlets in a decentralized MCC environment[C]//IEEE/ACM International Conference on Mobile Software Engineering and Systems. 2016:15-16.
[16]TAWALBEH L, JARARWEH Y, ABABNEH F, et al. Large scale Cloudlets deployment for efficient mobile cloud computing[J]. Journal ofNetworks, 2015, 10(1):70-76.
[17]SAAD H B, KASSAR M, SETHOM K. Utility-based Cloudlet selection in mobile cloud computing[C]//2016 Global Summit on Computer&Information Technology, 2016:91-96.
[18]CHILUKURI S, BOLLAPRAGADA S, KOMMINENI S, et al. RainCloud-Cloudlet selection for effective cyber foraging[C]//Wireless Communications and NETWORKING Conference. 2017.
[19]GAI K, QIU M, ZHAO H, et al. Dynamic energy-aware Cloudletbased mobile cloud computing model for green computing[J]. Journal of Network&Computer Applications, 2016, 59(C):46-54.
[20]ROY D G, DE D, MUKHERJEE A, et al. Application-aware Cloudlet selection for computation offloading in multi-Cloudlet environment[J].Journal of Supercomputing, 2017, 73(4):1-19.
[21]SHU G, ZHENG X, XU H, et al. Cloudlet-assisted heuristic offloading for mobile interactive applications[C]//IEEE International Conference on Mobile Cloud Computing,Services, and Engineering. 2017:66-73.
[22]LIU W, CAO J, QIU X, et al. Improving performance of mobile interactive data-streaming applications with multiple Cloudlets[C]//IEEE International Conference on Cloud Computing Technology and Science. IEEE Computer Society, 2014:46-53.
[23]RAVI A, PEDDOJU S K. Mobility managed energy efficient android mobile devices using Cloudlet[C]//Students'Technology Symposium.2014:402-407.
[24]PERRUCCI G P, FITZEK F H P, WIDMER J. Survey on energy consumption entities on the smartphone platform[C]//Vehicular Technology Conference. 2011:1-6.
[25]蒋廷耀,李庆华.DAG任务图的一种调度算法[J].小型微型计算机系统,2003, 24(10):1796-1799.JIANG T Y, LI Q H. A scheduling algorithm for dag task graphs[J]. Journal of Chinese Mini-Micro Computer Systems, 2003,24(10):1796-1799.
[26]GAREY M R, JOHNSON D S. Computers and intractability:a guide to the theory of NP-completeness[M]. W. H. Freeman, 1986.
[27]陈乃金,江建慧.融合面积估算和多目标优化的硬件任务划分算法[J].通信学报,2013(2):40-55.CHEN N J, JIANG J H. Hardware-task partitioning algorithm merged area estimation with multi-objective optimization[J]. Journal on Communications, 2013(2):40-55.
[28]LAM A Y S, LI V O K. Chemical-reaction-inspired metaheuristic for optimization[J]. IEEE Transactions on Evolutionary Computation,2010, 14(3):381-399.
[29]XU J, LAM A Y S, LI V O K. Chemical reaction optimization for task scheduling in grid computing[J]. IEEE Transactions on Parallel&Distributed Systems, 2011,22(10):1624-1631.
[30]JAMES J Q, LAM A Y S, LI V O K. Evolutionary artificial neural network based on chemical reaction optimization[C]//IEEE Congress on Evolutionary Computation. 2011:2083-2090.
[31]刘全,王晓燕,傅启明,等.双精英协同进化遗传算法[J].软件学报,2012, 23(4):765-775.LIU Q, WANG X Y, FU Q M, et al. Double elite coevolutionary genetic algorithm[J]. Journal of Software, 2012, 23(4):765-775.
[32]LAM A Y S, LI V O K, XU J. On the convergence of chemical reaction optimization for combinatorial optimization[J]. IEEE Transactions on Evolutionary Computation, 2013, 17(5):605-62
[33]PAN Q K, TASGETIREN M F, LIANG Y C. A discrete particle swarm optimization algorithm for the permutation flowshop sequencing problem with makespan criterion[M]. London:Springer,2007:19-31.