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首页 > 过刊浏览>2020年第29卷第3期 >11-19. DOI:10.15888/j.cnki.csa.007261
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面向云计算的任务调度算法综述
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基金项目:

国家重点研发项目（2018YFB1308600，2018YFB1308602）；国家自然科学基金（61272364）；广东高校省级重大科研项目（201612008QX，2016KTSCX167，2017KTSCX207，2018KTSCX288）；广东省学科建设专项资金（2013WYXM0122）；广东省自然科学基金（2016A030313384）；广东省大学生创新创业训练计划（201813177028，201813177046）；深圳市科技计划（JCYJ20170303140803747）；广东省大学生科技创新培育专项（pdjh2019b0581）；智能多媒体技术重点实验室（201762005）


Survey on Task Scheduling Algorithms for Cloud Computing
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    摘要:

    介绍了云计算，对任务调度在云计算中的地位做了分析，并由任务调度出发，对云计算任务调度算法的研究现状进行分类、梳理和总结.根据调度目标的不同，将算法分为单目标优化的任务调度算法和多目标的任务调度算法，对每类方法的代表性算法进行了分析介绍，并详细总结了每类方法的基本思想，对其优缺点做了分析、对比，并对改进方式进行了归纳.

    Abstract:

    Cloud computing is one of the emerging industries based on the Internet for commercial calculation model. It provides a quick and easy and reliable access to network resources. Cloud computing is introduced. The task scheduling in cloud computing is analyzed, and the research status of cloud computing task scheduling algorithm are classified and summarized according to different scheduling goal. The task scheduling algorithm can be divided into single objective optimization algorithm and multi-objective task scheduling algorithm. The representative algorithms of each method are analyzed, and the advantages and disadvantages of each algorithm are compared and summarized in detail, and the way of improvement is also inducted.

    参考文献
    [1] Wu FH, Wu QB, Tan YS. Workflow scheduling in cloud:A survey. The Journal of Supercomputing, 2015, 71(9):3373-3418.[doi:10.1007/s11227-015-1438-4
    [2] Sabi HM, Uzoka FME, Mlay SV. Staff perception towards cloud computing adoption at universities in a developing country. Education and Information Technologies, 2018, 23(5):1825-1848.[doi:10.1007/s10639-018-9692-8
    [3] Al-Dhuraibi Y, Paraiso F, Djarallah N, et al. Elasticity in cloud computing:State of the art and research challenges. IEEE Transactions on Services Computing, 2018, 11(2):430-447.[doi:10.1109/TSC.2017.2711009
    [4] Weinman J. The economics of pay-per-use pricing. IEEE Cloud Computing, 2018, 5(5):101-c3.[doi:10.1109/MCC.2018.053711671
    [5] 刘永.云计算技术研究综述.软件导刊, 2015, 14(9):4-6
    [6] Xu ZC, Liang WF, Xia QF. Efficient embedding of virtual networks to distributed clouds via exploring periodic resource demands. IEEE Transactions on Cloud Computing, 2018, 6(3):694-707.[doi:10.1109/TCC.2016.2535215
    [7] Li RX, Shen CL, He H, et al. A lightweight secure data sharing scheme for mobile cloud computing. IEEE Transactions on Cloud Computing, 2018, 6(2):344-357.[doi:10.1109/TCC.2017.2649685
    [8] Anushree B, Arul Xavier VM. Comparative analysis of latest task scheduling techniques in cloud computing environment. Proceedings of 2018 Second International Conference on Computing Methodologies and Communication. Erode, India. 2018. 608-611.
    [9] 王治东.云计算环境下任务调度研究综述.中国新通信, 2017, 19(9):78.[doi:10.3969/j.issn.1673-4866.2017.09.066
    [10] Arabnejad H, Barbosa JG. List scheduling algorithm for heterogeneous systems by an optimistic cost table. IEEE Transactions on Parallel and Distributed Systems, 2014, 25(3):682-694.[doi:10.1109/TPDS.2013.57
    [11] Wang HJ, Sinnen O. List-scheduling versus clus-ter-scheduling. IEEE Transactions on Parallel and Distributed Systems, 2018, 29(8):1736-1749.[doi:10.1109/TPDS.2018.2808959
    [12] Sonmez O, Yigitbasi N, Abrishami S, et al. Performance analysis of dynamic workflow scheduling in multicluster grids. Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing. Chicago, IL, USA. 2010. 49-60.
    [13] Singh P, Dutta M, Aggarwal N. A review of task scheduling based on meta-heuristics approach in cloud computing. Knowledge and Information Systems, 2017, 52(1):1-51.[doi:10.1007/s10115-017-1044-2
    [14] 徐健锐,朱会娟.基于自适应惩罚函数的云工作流调度协同进化遗传算法.计算机科学, 2018, 45(8):105-112
    [15] 李超,戴炳荣,旷志光,等.云计算环境下基于改进遗传算法的多维约束任务调度研究.小型微型计算机系统, 2017, 38(9):1945-1949.[doi:10.3969/j.issn.1000-1220.2017.09.005
    [16] 任金霞,黄艺培,钟小康.基于遗传算法的云任务调度改进算法.江西理工大学学报, 2018, 39(3):90-94
    [17] 张浩为,谢军伟,张昭建,等.基于混合自适应遗传算法的相控阵雷达任务调度.兵工学报, 2017, 38(9):1761-1770.[doi:10.3969/j.issn.1000-1093.2017.09.013
    [18] 王国豪,李庆华,刘安丰.多目标最优化云工作流调度进化遗传算法.计算机科学, 2018, 45(5):31-37, 48
    [19] Dorigo M, Gambardella LM. Ant colony system:A coop-erative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation, 1997, 1(1):53-66.[doi:10.1109/4235.585892
    [20] Dorigo M, Stützle T. Ant colony optimization:Overview and recent advances. Gendreau M, Potvin JY. Handbook of Metaheuristics. Cham:Springer, 2019. 311-351.
    [21] 李琳,应时,董波.一种求解面向服务软件部署优化问题的多目标蚁群算法.中南大学学报(自然科学版), 2017, 48(9):2376-2387.[doi:10.11817/j.issn.1672-7207.2017.09.017
    [22] Moon YJ, Yu HC, Gil JM, et al. A slave ants based ant col-ony optimization algorithm for task scheduling in cloud computing environments. Human-Centric Computing and Information Sciences, 2017, 7(1):28.[doi:10.1186/s13673-017-0109-2
    [23] Mao XM. Study on ant colony optimization algorithm for "one-day tour" traffic line. Cluster Computing, 2019, 22(S2):3673-3680.[doi:10.1007/s10586-018-2217-9
    [24] Zhang Q, Zhang CS. An improved ant colony optimization algorithm with strengthened pheromone updating mechanism for constraint satisfaction problem. Neural Computing and Applications, 2018, 30(10):3209-3220.[doi:10.1007/s00521-017-2912-0
    [25] 林哲骋,许力.一种应用于激光焊接轨迹规划的改进蚁群算法.焊接学报, 2018, 39(1):107-110.[doi:10.12073/j.hjxb.2018390024
    [26] Kennedy J, Eberhart R. Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks. Perth, Australia. 1995. 1942-1948.
    [27] Sammut C, Webb GI. Encyclopedia of Machine Learning and Data Mining. Boston, MA, USA:Springer, 2017:1-10.
    [28] 宋寒冰.云计算环境下基于改进PSO算法的任务调度研究[硕士学位论文].长春:吉林大学, 2017.
    [29] Guo LZ, Wang YJ, Zhao SG, et al. Particle swarm opti-mization embedded in variable neighborhood search for task scheduling in cloud computing. Journal of Donghua University (English Edition), 2013, 30(2):145-152
    [30] 赵莉.基于改进量子粒子群算法的云计算资源调度.南京理工大学学报, 2016, 40(2):223-228
    [31] Liu L, Shan L, Jiang C, et al. Parameter identification of the fractional-order systems based on a modified PSO algorithm. Journal of Southeast University (English Edition), 2018, 34(1):6-14
    [32] 方昕.一种新型启发式PSO算法求解市区最优路径规划研究.计算机与数字工程, 2018, 46(2):270-275.[doi:10.3969/j.issn.1672-9722.2018.02.013
    [33] 邓先礼,魏波,曾辉,等.基于多种群的自适应迁移PSO算法.电子学报, 2018, 46(8):1858-1865.[doi:10.3969/j.issn.0372-2112.2018.08.009
    [34] 高健,高培.一种改进的模拟退火算法求解中学排课问题.工业计量, 2018, 28(4):72-75, 91
    [35] 姚壹壹,王玲鹏,金科扬,等.基于模拟退火算法最优物流配送问题的应用.宁波工程学院学报, 2018, 30(1):39-44.[doi:10.3969/j.issn.1008-7109.2018.01.007
    [36] 刘新星,张贞凯,费晓.模拟退火算法的共享孔径多波束形成.电光与控制, 2018, 25(11):57-61.[doi:10.3969/j.issn.1671-637X.2018.11.011
    [37] 董明佶,林宝军,刘迎春,等.基于多目标模拟退火算法的导航卫星激光星间链路拓扑动态优化.中国激光, 2018, 45(7):0706004
    [38] 黄海松,刘凯,初光勇.改进模拟退火算法在柔性调度中的应用.组合机床与自动化加工技术, 2018,(2):148-151, 156
    [39] 杜大华,贺尔铭,李磊.改进模拟退火算法的喷管动力学模型修正.宇航学报, 2018, 39(6):632-638
    [40] Yu W, Liang F, He XF, et al. A survey on the edge computing for the internet of things. IEEE Access, 2018, 6:6900-6919.[doi:10.1109/ACCESS.2017.2778504
    [41] 施巍松,张星洲,王一帆,等.边缘计算:现状与展望.计算机研究与发展, 2019, 56(1):69-89.[doi:10.7544/issn1000-1239.2019.20180760
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杨戈,赵鑫,黄静.面向云计算的任务调度算法综述.计算机系统应用,2020,29(3):11-19

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  • 收稿日期:2019-07-04
  • 最后修改日期:2019-07-23
  • 在线发布日期: 2020-03-02
  • 出版日期: 2020-03-15
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