基于不确定服务质量感知的云服务组合方法

doi:10.11772/j.issn.1001-9081.2018041187

计算机应用 ›› 2018, Vol. 38 ›› Issue (10): 2753-2758.DOI: 10.11772/j.issn.1001-9081.2018041187

• 2018中国粒计算与知识发现学术会议(CGCKD 2018)论文 • 下一篇

基于不确定服务质量感知的云服务组合方法

王思臣¹, 涂辉², 张以文¹

1. 计算智能与信号处理教育部重点实验室(安徽大学), 合肥 230031;
2. 瓦斯治理国家工程研究中心(淮南矿业集团), 安徽淮南 232001

收稿日期:2018-03-28 修回日期:2018-06-12 出版日期:2018-10-10 发布日期:2018-10-13
通讯作者: 张以文
作者简介:王思臣(1991-),男,河南商丘人,硕士研究生,主要研究方向:服务计算、进化计算;涂辉(1984-),男,江西宜春人,硕士研究生,主要研究方向:大数据、云计算;张以文(1976-),男,安徽马鞍山人,副教授,博士,CCF会员,主要研究方向:服务计算、大数据、云计算。
基金资助:
国家自然科学基金资助项目（61872002）；国家科技支撑计划项目（2015BAK24B01）；安徽省自然科学基金资助项目（1808085MF197）；安徽省科技重大专项（16030901062）。

Cloud service composition method based on uncertain QoS-aware ness

WANG Sichen¹, TU Hui², ZHANG Yiwen¹

1. Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education(Anhui University), Hefei Anhui 230031, China;
2. National Engineering Research Center for Coal Mine Gas Controlling(Huainan Mining(Group) Company Limited), Huainan Anhui 232001, China

Received:2018-03-28 Revised:2018-06-12 Online:2018-10-10 Published:2018-10-13
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61872002), the National Key Technology R&D Program of China (2015BAK24B01), the Natural Science Foundation of Anhui Province (1808085MF197), the Science and Technology Major of the Ministry Project of Anhui Province (16030901062).

摘要/Abstract

摘要： 针对不确定服务质量（QoS）感知的云服务组合优化问题的求解，提出一种不定长时间序列（ULST）模型和锦标赛策略的改进遗传算法（T-GA）。首先，基于用户对服务不同时间段的访问规律，将服务质量的长期变化构建为不定长时间序列模型，该模型能够准确地描述一段时间内用户对服务的真实QoS访问记录。其次，提出一种基于不确定QoS模型的改进遗传算法，该算法采用锦标赛选择策略代替基本遗传算法中的轮盘赌选择策略。最后，在真实数据上进行了大量实验，所提的不定长时间序列模型能够有效地解决不确定QoS感知云服务组合问题，而锦标赛策略的改进遗传算法在寻优结果和稳定性方面均优于基于精英选择策略的遗传算法（E-GA）算法，且运行速度提高近1倍，是可行、高效且稳定的算法。

关键词: 不确定QoS, 长期, 服务组合, 优化算法

Abstract: To solve the problem of uncertain Quality of Service (QoS)-aware cloud service composition optimization, an Uncertain-Long Time Series (ULST) model and Tournament strategy based Genetic Algorithm (T-GA) was proposed. Firstly, based on different access rules of users to services in different periods, the long-term change of QoS was modeled as an uncertain-long time series, which can accurately describe the users' actual QoS access record to service over a period of time. Secondly, an improved genetic algorithm based on uncertain QoS model was proposed, which used tournament strategy instead of basic roulette wheel selection strategy. Finally, a lot of experiments were carried out on real data. The uncertain-long time series model can effectively solve the problem of uncertain QoS-aware cloud service composition; the proposed T-GA is superior to the Genetic Algorithm based on Elite selection strategy (E-GA) in optimization results and stability, and the execution speed is improved by almost one time, which is a feasible, high efficient and stable algorithm.

Key words: uncertain QoS, long-term, service composition, optimization algorithm

中图分类号:

TP18

王思臣, 涂辉, 张以文. 基于不确定服务质量感知的云服务组合方法[J]. 计算机应用, 2018, 38(10): 2753-2758.

WANG Sichen, TU Hui, ZHANG Yiwen. Cloud service composition method based on uncertain QoS-aware ness[J]. Journal of Computer Applications, 2018, 38(10): 2753-2758.

参考文献

[1] ARDAGNA D, PERNICI B. Adaptive service composition in flexible processes[J]. IEEE Transactions on Software Engineering, 2007, 33(6):369-384.
[2] HUANG A F M, LAN C W, YANG S J H. An optimal QoS-based Web service selection scheme[J]. Information Sciences, 2009, 179(19):3309-3322.
[3] ZHANG Y W, CUI G M, ZHAO S, et al. IFOA4WSC:a quick and effective algorithm for QoS-aware service composition[J]. International Journal of Web and Grid Services, 2016, 12(1):81-108.
[4] 倪志伟, 方清华, 李蓉蓉, 等.改进蚁群算法在基于服务质量的Web服务组合优化中的应用[J]. 计算机应用, 2015, 35(8):2238-2243. (NI Z W, FANG Q H, LI R R, et al. Improved ant colony optimization for QoS-based Web service composition optimization[J]. Journal of Computer Applications, 2015, 35(8):2238-2243.)
[5] CREMENR M, SUCIU M, PALLEZ D, et al. Comparative analysis of multi-objective evolutionary algorithms for QoS-aware Web service composition[J]. Applied Soft Computing, 2016, 39:124-139.
[6] WAGNER F, KLEIN A, KLOPPER B, et al. Multi-objective service composition with time and input-dependent QoS[C]//Proceedings of the 2012 IEEE 19th International Conference on Web Services. Piscataway, NJ:IEEE, 2012:234-241.
[7] YILMAZ A, KARAGOZ P. Improved genetic algorithm based approach for QoS aware Web service composition[C]//Proceedings of the 2014 IEEE International Conference on Web Services. Piscataway, NJ:IEEE, 2014:463-470.
[8] NIU S, ZOU G, GAN Y, et al. Towards uncertain QoS-aware service composition via multi-objective optimization[C]//Proceedings of the 2017 IEEE International Conference on Web Services. Piscataway, NJ:IEEE, 2017:894-897.
[9] LIU S C, WEI Y, TANG K, et al. QoS-aware long-term based service composition in cloud computing[C]//Proceedings of the 2015 IEEE Congress on Evolutionary Computation. Piscataway, NJ:IEEE, 2015:3362-3369.
[10] WAN C, WANG H. Uncertainty-aware QoS description and selection model for Web services[C]//SCC 2007:Proceedings of the 2007 IEEE International Conference on Services Computing. Piscataway, NJ:IEEE, 2007:154-161.
[11] YE Z, BOUGUETTAYA A, ZHOU X. QoS-aware cloud service composition based on economic models[C]//ICSOC 2012:Proceedings of the 2012 International Conference on Service-Oriented Computing. Berlin:Springer, 2012:111-126.
[12] YE Z, BOUGUETTAYA A, ZHOU X. QoS-aware cloud service composition using time series[C]//ICSOC 2013:Proceedings of the 2013 International Conference on Service-Oriented Computing. Berlin:Springer, 2013:9-22.
[13] YE Z, MISTRY S, BOUGUETTAYA A, et al. Long-term QoS-aware cloud service composition using multivariate time series analysis[J]. IEEE Transactions on Services Computing, 2016, 9(3):382-393.
[14] LABBACI H, MEDJAHED B, AKLOUF Y. A deep learning approach for long term QoS-compliant service composition[C]//ICSOC 2017:Proceedings of the 2017 International Conference on Service-Oriented Computing. Berlin:Springer, 2017:287-294.
[15] 肖文娟, 段玉聪.基于时间序列的感知QoS的云服务组合[J]. 计算机工程与科学, 2014, 36(11):2061-2066. (XIAO W J, DUAN Y C. Time series-based QoS aware cloud service composition[J]. Computer Engineering & Science, 2014, 36(11):2061-2066).

基于不确定服务质量感知的云服务组合方法

Cloud service composition method based on uncertain QoS-aware ness

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	陈俊, 何庆. 基于余弦相似度的改进蝴蝶优化算法[J]. 计算机应用, 2021, 41(9): 2668-2677.
[2]	李开荣, 刘爽, 胡倩倩, 唐亦媛. 基于转角约束的改进蚁群优化算法路径规划[J]. 计算机应用, 2021, 41(9): 2560-2568.
[3]	李蒙蒙, 秦伟, 刘艺, 刁兴春. 结合头脑风暴优化的混合蚁群优化算法[J]. 计算机应用, 2021, 41(8): 2412-2417.
[4]	汤安迪, 韩统, 徐登武, 谢磊. 混沌精英哈里斯鹰优化算法[J]. 计算机应用, 2021, 41(8): 2265-2272.
[5]	汤安迪, 韩统, 徐登武, 谢磊. 基于混沌麻雀搜索算法的无人机航迹规划方法[J]. 计算机应用, 2021, 41(7): 2128-2136.
[6]	赵秋云, 魏乐, 舒红平. 面向制造任务的云制造虚拟车间构造方法[J]. 计算机应用, 2021, 41(7): 2003-2011.
[7]	张盟, 郭健全. 需求和回收不确定的闭环供应链渠道结构选择[J]. 计算机应用, 2021, 41(7): 2100-2107.
[8]	贾鹤鸣, 姜子超, 李瑶, 孙康健. 基于改进斑点鬣狗优化算法的同步优化特征选择[J]. 计算机应用, 2021, 41(5): 1290-1298.
[9]	许红亮, 杨桂芹, 蒋占军. 基于软件定义网络的数据中心自适应多路径负载均衡算法[J]. 计算机应用, 2021, 41(4): 1160-1164.
[10]	许飞雪, 刘勤明, 欧阳海玲, 叶春明. 基于性能合同的多部件系统维修策略模型优化[J]. 计算机应用, 2021, 41(4): 1184-1191.
[11]	冉家敏, 倪志伟, 彭鹏, 朱旭辉. 考虑空间众包工作者服务质量的任务分配策略及其萤火虫群优化算法求解[J]. 计算机应用, 2021, 41(3): 794-802.
[12]	王泽昆, 贺毅朝, 李焕哲, 张发展. 基于新颖S型转换函数的二进制粒子群优化算法求解具有单连续变量的背包问题[J]. 计算机应用, 2021, 41(2): 461-469.
[13]	樊小毛, 熊红林, 赵淦森. 带约束的清洁排班问题模型及其求解[J]. 计算机应用, 2021, 41(2): 577-582.
[14]	孙建强, 许少华. 基于可微神经计算机和贝叶斯网络的知识推理方法[J]. 计算机应用, 2021, 41(2): 337-342.
[15]	董海, 吴瑶, 齐新娜. 基于改进鲸鱼优化算法的血液供应链网络多目标鲁棒优化设计[J]. 计算机应用, 2021, 41(10): 3063-3069.