计算机科学 ›› 2019, Vol. 46 ›› Issue (11A): 354-358.
黄引豪1,2, 马郓3, 林兵2,4, 於志勇1,2, 陈星1,2
HUANG Yin-hao1,2, MA Yun3, LIN Bing2,4, YU Zhi-yong1,2, CHEN Xing1,2
摘要: 科学工作流在混合云中执行会产生大量的跨数据中心传输,造成严重的传输时延及代价。为了对混合云环境下的科学工作流数据进行合理布局,兼顾公有云和私有云的优势,优化数据布局代价,提出了一种基于遗传粒子群优化混合算法(GAPSO)的数据布局策略。该方法考虑了公有云数据中心与私有云数据中心的不同特点(如存储容量、存储代价等因素以及数据传输时延约束)对传输代价的影响,并结合遗传算法与粒子群优化算法的优点,生成科学工作流的布局策略。实验结果表明,基于GAPSO的数据布局策略能够有效减少混合云中科学工作流运行时的数据布局代价。
中图分类号:
[1]SZABO C,SHENG Q Z,KROEGER T,et al.Science in theCloud:Allocation and Execution of Data-IntensiveScientific Workflows[J].Journal of Grid Computing,2014,12(2):245-264. [2]WEISS A.Computing in the clouds[J].Networker,2007,11(4):16-25. [3]ZHANG X,ZHANG Y,ZHAO X,et al.SmartRelationship:aVM relationship detection framework for cloud management[C]∥Asia-pacific Symposium on Internetware.2014. [4]CHEN X,ZHANG Y,ZHANG X,et al.Towards runtime model based integrated management of cloud resources[C]∥Asia-pacific Symposium on Internetware.ACM,2013. [5]ZHANG X,CHEN X,ZHANG Y,et al.Runtime Model Based Management of Diverse Cloud Resources[C]∥International Conference on Model Driven Engineering Languages and Systems.Springer,Berlin,Heidelberg,2013. [6]HUANG G,CHEN X,ZHANG Y,et al.Towards Architecture-based Management of Platforms in Cloud[J].中国计算机科学前沿(英文版),2012,6(4):388-397. [7]ABRISHAMI H,REZAEIAN A,TOUSI G K,et al.Scheduling in hybrid cloud to maintain data privacy[C]∥FifthInternational Conference on Innovative Computing Technology.IEEE,2015:83-88. [8]AN B,ZHANG X,TSUGAWA M,et al.Towards a Model-Defined Cloud-of-Clouds[C]∥Collaboration & Internet Computing.IEEE,2016. [9]ARMBRUST M,FOX A,GRIFFITH R,et al.Above theclouds:A Berkeley view of cloud computing[R].No.UCB/EECS-2009-28,Berkeley:Department of Electrical Engeering and Computer Sciences,University of California,2009. [10]FU J,WANG J C,LU J,et al.Research on meteorology indices forecasting framework based on hybrid cloud computingplatforms[C]∥Proc.of the Ubiquitous Information Technologies and Applications.Netherlands:Springer-Verlag,2013:727-735. [11]陈晓,赵晶玲.大数据处理中混合型聚类算法的研究与实现[J].信息网络安全,2015(4):45-49. [12]YUAN D,YANG Y,LIU X,et al.A data placement strategy in scientific cloud workflows[J].Future GenerationComputer Systems,2010,26(8):1200-1214. [13]DENG K,REN K,ZHU M,et al.A Data and Task Co-scheduling Algorithm for Scientific Cloud Workflows[J].IEEE Transactions on Cloud Computing,2015:1-1. [14]WANG M,ZHANG J,DONG F,et al.Data Placement and Task Scheduling Optimization for Data IntensiveScientific Workflow in Multiple Data Centers Environment[C]∥International Conference on Advanced Cloud & Big Data.IEEE,2014:77-84. [15]程慧敏,李学俊,吴洋,等.云环境下基于多目标优化的科学工作流数据布局策略[J].计算机应用与软件,2017,34(3):1-6. [16]王东亮,衣俊艳,李时慧,等.融合负载均衡和蝙蝠算法的云计算任务调度[J].信息网络安全,2017(1):23-28. [17]ZHAO Q,XIONG C,ZHAO X,et al.A Data Placement Strategy for Data-Intensive Scientific Workflows inCloud[C]∥IEEE/ACM International Symposium on Cluster,Cloud and Grid Computing.IEEE,2015:928-934. [18]ZHANG X X,HU Z G,ZHENG M G,et al.A novel cloud model based data placement strategy for data-intensive application in clouds[J].Computers and Electrical Engineering,2018. [19]彭晓波,桂卫华,黄志武,等.GAPSO:一种高效的遗传粒子混合算法及其应用[J].系统仿真学报,2008,20(18):5025-5031. [20]马小平.私有云存储系统的设计与实现[D].成都:电子科技大学,2014. [21]林兵,郭文忠,陈国龙.多云环境下带截止日期约束的科学工作流调度策略[J].通信学报,2018,39(1):56-69. [22]KENNEDY J,EBERHART R.Particle swarm optimization[C]∥IEEE International Conference on Neural Networks.IEEE,2002:1942-1948. [23]HOLLAND J H.Adaptation in Natural and Artificial Systems[M].Ann Arbor,Michigan:University of Michigan Press,1975. [24]SHI Y,EBERHART R.A modified particle swarm optimizer[C]∥IEEE International Conference on Evolutionary Computation Proceedings.IEEE World Congress on Computational Intelligence,1998:69-73. [25]BHARATHI S,CHERVENAK A,DEELMAN E,et al.Characterization of scientific workflows[C]∥Third Workshop onWorkflows in Support of Large-Scale Science.IEEE,2008:1-10. [26]CUI L,ZHANG J,YUE L,et al.A Genetic Algorithm Based Data Replica Placement Strategy for Scientific Applications in Clouds[J].IEEE Transactions on Services Computing,2015. |
[1] | 柳鹏, 刘波, 周娜琴, 彭心怡, 林伟伟. 混合云工作流调度综述 Survey of Hybrid Cloud Workflow Scheduling 计算机科学, 2022, 49(5): 235-243. https://doi.org/10.11896/jsjkx.210300303 |
[2] | 严磊, 张功萱, 王添, 寇小勇, 王国洪. 混合云下具有交付期约束的众包任务调度算法 Scheduling Algorithm for Bag-of-Tasks with Due Date Constraints on Hybrid Clouds 计算机科学, 2022, 49(5): 244-249. https://doi.org/10.11896/jsjkx.210300120 |
[3] | 季琰, 戴华, 姜莹莹, 杨庚, 易训. 面向混合云的可并行多关键词Top-k密文检索技术 Parallel Multi-keyword Top-k Search Scheme over Encrypted Data in Hybrid Clouds 计算机科学, 2021, 48(5): 320-327. https://doi.org/10.11896/jsjkx.200300160 |
[4] | 刘漳辉, 赵旭, 林兵, 陈星. 混合云环境下基于模糊理论的科学工作流数据布局策略 Data Placement Strategy of Scientific Workflow Based on Fuzzy Theory in Hybrid Cloud 计算机科学, 2021, 48(11): 199-207. https://doi.org/10.11896/jsjkx.200900009 |
[5] | 黄冬梅, 杜艳玲, 贺琪, 随宏运, 李瑶. 基于多属性最优化的海洋监测数据副本布局策略 Marine Monitoring Data Replica Layout Strategy Based on Multiple Attribute Optimization 计算机科学, 2018, 45(6): 72-75. https://doi.org/10.11896/j.issn.1002-137X.2018.06.012 |
[6] | 张桂鹏, 陈平华. 一种混合云环境下基于Merkle哈希树的数据安全去重方案 Secure Data Deduplication Scheme Based on Merkle Hash Tree in HybridCloud Storage Environments 计算机科学, 2018, 45(11): 187-192. https://doi.org/10.11896/j.issn.1002-137X.2018.11.029 |
[7] | 汪学舜,余少华,戴锦友. 一种虚拟化深度包检测部署机制 Virtualization Deep Packet Inspection Deployment Method 计算机科学, 2017, 44(8): 90-94. https://doi.org/10.11896/j.issn.1002-137X.2017.08.017 |
[8] | 缪嘉嘉,付印金,毛捍东. KingCloud:智能对象归档系统 KingCloud:Object Oriented Archiving System 计算机科学, 2016, 43(Z11): 575-577. https://doi.org/10.11896/j.issn.1002-137X.2016.11A.130 |
[9] | 范菁,沈杰,熊丽荣. 混合云环境中数据敏感工作流调度 Scheduling Data Sensitive Workflow in Hybrid Cloud 计算机科学, 2015, 42(Z11): 400-405. |
[10] | 王宗江,郑秋生,曹健. 混合云中的一个高效协调器 Efficient Coordinator in Hybrid Cloud 计算机科学, 2015, 42(1): 92-95. https://doi.org/10.11896/j.issn.1002-137X.2015.01.022 |
[11] | 杨敏 王刚 刘璟 陈北莲. 用双目标加权遗传算法解决网络磁盘阵列系统下校验散布布局优化问题的研究 计算机科学, 2005, 32(5): 73-75. |
|