运用备份服务位置和概率QoS模型的Web服务组合算法

doi:10.11959/j.issn.1000-0801.2016249

摘要/Abstract

摘要：

针对工作流服务数的增加过程中最初规划的潜在成本较高以及很多服务组合算法可靠性不足等问题，提出了一种利用备份服务位置和概率服务质量（QoS）模型的服务组合算法。该算法计算服务集群的QoS优化选择，为每个服务包含足够数量的备份服务，且考虑了备份服务位置，以便在单个故障点上进行评估。由于这些备份服务分布均匀，防止了任务失败的发生。对于服务选择问题，采用一种改进的多目标优化（MOO）算法，利用聚类和QoS模型来计算可行解集合。仿真实验采用JMETAL 3.1框架，评估备份服务位置的收益以及算法的可靠性。结果表明，相比于其他MOO算法，提出的算法可靠性更高，从备份服务位置所获得的收益更高。

关键词: 工作流, 服务组合, 概率服务质量, 多目标优化, 聚类

Abstract:

In the process of increasing the number of work-flow services, as the potential cost of the initial planning is high, and the reliability of many service composition algorithms is not enough. A service composition algorithm based on the location of backup service and probabilistic quality-of-service（QoS）model was proposed. A service cluster based QoS optimization selection for each service was computed which contained a sufficient number of backup services. The location of backup service was considered to be evaluated at failure of a single point. Because the backup service was distributed evenly, the occurrence of the failure of the task was presented. For the service selection problem, an improved multiple objective optimization（MOO）algorithm was adopted to calculate the feasible solution set using clustering and QoS model. JMETAL 3.1 framework was used to assess the returns of the backup service location and the reliability of the algorithms. The results show that compared to other MOO algorithms, the proposed algorithm is more reliable and the gain from the backup service position is higher.

Key words: workflow, service composition, probabilistic quality-of-service, multiple objective optimization, clustering

文华. 运用备份服务位置和概率QoS模型的Web服务组合算法[J]. 电信科学, 2016, 32(10): 116-125.

Hua WEN. Web services composition algorithm based on the location of backup service and probabilistic QoS model[J]. Telecommunications Science, 2016, 32(10): 116-125.

图/表 10

图1

图2

表1

表2

图

图4

图5

图6

图7

图8

参考文献 15

[1]	曹瑞彤，曹旭 . 一种基于支持向量机的服务质量评估方案[J]. 电信科学， 2014,30(2):123-126. CAO R T , CAO X . A scheme of QoS evaluation based on support vector machine[J]. Telecommunications Science, 2014,30(2):123-126.
[2]	徐战，王劲林，吴刚，等. 基于路径QoS加权分解的工作流调度算法[J]. 系统仿真学报， 2012,24(5):1035-1040. XU Z , WANG J L , WU G , et al. Workflow scheduling algorithm based on weighted decomposition of paths' QoS[J]. Journal of System Simulation, 2012,24(5):1035-1040.
[3]	ISHIKAWA F , KATAFUCHI S , WAGNER F , et al. Bridging the gap between semantic Web services composition and common implementation architectures [C]// IEEE International Conference on Services Computing, July 4-9,2011, Washington, DC, USA. New Jersey:IEEE Press, 2011: 152-159.
[4]	GUINARD D , TRIFA V , SPIESS P , et al. Discovery and on-demand provisioning of real-world web services[C]// IEEE International Conference on Web Services, July 6-10,2009, Los Angeles, CA, USA. New Jersey:IEEE Press, 2009: 583-590.
[5]	李强 . 网构化软件服务的动态替换技术研究[D]. 长沙：中南大学， 2014. LI Q . Research on dynamic replacement technology of Web based software service[D]. Changsha:Central South University, 2014.
[6]	LIN K J , ZHANG J , ZHAI Y . An efficient approach for service process reconfiguration in SOA with end-to-end QoS constraints[C]// IEEE Conference on Commerce and Enterprise Computing, July 20-23,2009, Vienna, Austria. New Jersey:IEEE Press, 2009: 597-600.
[7]	沈虹，李小平 . 带准备时间和截止期约束的云服务工作流调度算法[J]. 通信学报， 2015,36(6):183-192. SHEN H , LI X P . Algorithm for the cloud service workflow scheduling with set up time and deadline constraints[J]. Journal on Communications, 2015,36(6):183-192.
[8]	胡建强，李涓子，廖桂平 . 一种基于多维服务质量的局部最优服务选择模型[J]. 计算机学报， 2010,33(3):526-534. HU J Q , LI J Z , LIAO G P . A multi-Qos based local optima model of service selection[J]. Chinese Journal of Computers, 2010,33(3):526-534.
[9]	KLEIN A , ISHIKAWA F , HONIDEN S . Efficient heuristic approach with improved time complexity for QoS-aware service composition[C]// IEEE International Conference on Web Services, July 4-9,2011, Washington, DC, USA. New Jersey:IEEE Press, 2011: 436-443.
[10]	MONTIELNELSON J A . Multi-objective genetic algorithms applied to low power pressure micro-sensor design[J]. Engineering Computations, 2014,30(30): 1128-1146.
[11]	赵莹映，章韵 . 基于推理机制的逻辑语言在Web服务组合机制中的应用[J]. 电信科学， 2012,28(12):70-75. ZHAO Y Y , ZHANG Y . Application of the logic language based on reasoning mechanism in Web composite service[J]. Telecommunications Science, 2012,28(12):70-75.
[12]	黄宣国 . 凸函数与琴生不等式[M] 上海:上海教育出版社, 1991. HUANG X G . Convex function and the inequality of birth[M]. Shanghai:Shanghai Education Publishing House, 1991.
	周明 . 遗传算法原理及应用[M] 北京:国防工业出版社, 1999. ZHOU M . Principle and application of genetic algorithm[M]. Beijing:National Defence Industry Press, 1999.
[14]	陆彦坤 . 使用精英策略的多目标遗传算法的研究[D]. 广州：中山大学， 2010. LU Y K . Research on the multi objective genetic algorithm using the elite strategy[D]. Guangzhou: Sun Yat-sen University, 2010.
[15]	TRAN V X , TSUJI H . QoS based ranking for Web services: fuzzy approaches[C]// International Conference on Next Generation Web Services Practices, IEEE Computer Society, July 6-10,2008, Washington DC,USA. New Jersey:IEEE Press, 2008: 77-82.

服务	组合部分
S₁₁	{S₂₁,S₂₂,S₂₄}
S₁₂	{S₂₂,S₂₄}
S₁₃	{S₃₂₂,S₂₃,S₂₄}
S₁₄	{S₂₄,S₂₅}
S₁₅	{S₂₁,S₂₄,S₂₅}

		QoS				聚合集群QoS
服务	价格/元	时间/ms	可靠性	节点	价格：（[p_best,p_worst],E[p]）/元	时间：（[t_best,t_worst],E[t]）/ms	可靠性
S₁₁	2	290	70%	N₁₁	（[2,2],2）	[290,290],290）	70%
S₁₂	3	240	30%	N₁₂	（[3,10],5.5）	（[241,710],481）	83%
S₁₃	5	180	20%	N₁₃	（[5,5],5）	（[180,180],180）	20%

[1]	刘昊双, 张永, 曹莹波. 基于K-means聚类的子结构相关适配迁移学习方法[J]. 电信科学, 2023, 39(3): 124-134.
[2]	朱洪涛, 郭庆. 基于用户群组的低轨卫星网络多星切换策略[J]. 电信科学, 2022, 38(4): 39-48.
[3]	赵海波, 相志军, 肖林松. 基于异构数据的电力短期负荷大数据预测方案[J]. 电信科学, 2022, 38(12): 103-111.
[4]	金宁, 王庆扬. 基于聚类算法的6G典型应用场景研究[J]. 电信科学, 2022, 38(1): 121-131.
[5]	陆勰, 徐雷, 张曼君. 基于聚类的安全分级虚拟网络映射方法[J]. 电信科学, 2021, 37(9): 112-117.
[6]	刘子豪, 贾小军, 张素兰, 徐志玲, 张俊. 一种融合MeanShift聚类分析和卷积神经网络的Vibe++背景分割方法[J]. 电信科学, 2021, 37(3): 133-145.
[7]	吴季桦, 朱鹏宇, 吴子辰, 顾彬, 洪涛, 郭波, 王晶, 王敬宇. 基于无监督聚类和频繁子图挖掘的电力通信网缺陷诊断与自动派单[J]. 电信科学, 2021, 37(11): 51-63.
[8]	李想,李原,张子飞,杨哲. 基于密度聚类的网络性能故障大数据分析方法[J]. 电信科学, 2020, 36(9): 51-58.
[9]	李亚杰,赵永利,刘守东,张杰. 基于人工智能的光纤非线性均衡算法研究概述[J]. 电信科学, 2020, 36(3): 61-70.
[10]	孙洋,粟栗,张星,王峰生,杜海涛. 基于子语义空间的挖掘短文本策略方法[J]. 电信科学, 2020, 36(3): 83-94.
[11]	高永梅,程冠杰. 基于边缘计算的数据密集型服务部署[J]. 电信科学, 2019, 35(7): 87-99.
[12]	江俊, 黄骅, 任条娟, 张登辉. 基于峰值密度聚类的电信业投诉热点话题检测方法[J]. 电信科学, 2019, 35(5): 97-103.
[13]	马大燕. 基于自动聚类模型的输电线路外力破坏预警预测[J]. 电信科学, 2019, 35(3): 135-139.
[14]	包晓安,曹云棣,张娜,钱俊彦,曹建文. 基于格分布方差的多目标云工作流调度算法[J]. 电信科学, 2019, 35(2): 1-13.
[15]	钟其柱, 吴修权, 罗耀满. 基于MDT智能分析LTE无线干扰的研究与应用[J]. 电信科学, 2019, 35(10): 130-136.