QoS decomposition for service composition using genetic algorithm
QoS decomposition for service composition using genetic algorithm
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DOI：
10.1016/j.asoc.2012.12.033
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发表时间：
2013-07
期刊：
影响因子：
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通讯作者：
F. Mardukhi;N. Nematbakhsh;K. Zamanifar;A. Barati
中科院分区：
文献类型：
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作者：
F. Mardukhi;N. Nematbakhsh;K. Zamanifar;A. Barati
近年来，大量的研究致力于优化QoS感知的服务组合。这旨在从所有可能的服务组合中选择最优的组合服务，关于用户的端到端的质量要求。现有的解决方案往往采用全局优化方法，这并没有表现出有前途的性能。而且，这些方法的复杂性广泛地取决于可用的web服务的数量，这些web服务随着因特网的增长而沿着不断增加。此外，局部优化方法很少使用，因为它们可能违反全局约束。在本文中，我们提出了一个自顶向下的结构，命名为质量约束分解（QCD）在这里，分解的全局约束到局部约束，使用遗传算法（GA）。然后通过简单的线性搜索为每个任务选择最佳Web服务。与现有的方法相比，QCD方法主要依赖于有限的任务集，这是相当不复杂的，特别是在动态分布的服务组合的情况下。实验结果，基于一个著名的数据集的Web服务（QWS），显示的QCD方法在计算时间方面的优势，考虑到Web服务的数量。
Recently, a lot of research has been dedicated to optimizing the QoS-aware service composition. This aims at selecting the optimal composed service from all possible service combinations regarding user's end-to-end quality requirements. Existing solutions often employ the global optimization approach, which does not show promising performance. Also, the complexity of such methods extensively depends on the number of available web-services, which continuously increase along with the growth of the Internet. Besides, the local optimization approaches have been rarely utilized, since they may violate the global constraints. In this paper, we propose a top-down structure, named quality constraints decomposition (QCD) here, to decompose the global constraints into the local constraints, using the genetic algorithm (GA). Then the best web service for each task is selected through a simple linear search. In contrast to existing methods, the QCD approach mainly depends on a limited set of tasks, which is considerably less complex, especially in the case of dynamically distributed service composition. Experimental results, based on a well-known data set of web services (QWSs), show the advantages of the QCD method in terms of computation time, considering the number of web services.