Correlation-aware QoS modeling and manufacturing cloud service composition
Correlation-aware QoS modeling and manufacturing cloud service composition
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DOI：
10.1007/s10845-015-1080-2
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发表时间：
2015-04
影响因子：
8.3
通讯作者：
Hong Jin;Xifan Yao;Yong Chen
中科院分区：
文献类型：
--
作者：
Hong Jin;Xifan Yao;Yong Chen
近年来，云制造引起了学术界和工业界的广泛关注。制造云服务的组合与优化是云制造资源优化配置的关键。由于存在许多具有相似功能但服务质量（QoS）不同的制造云服务，并且它们之间存在潜在的质量相关性，因此在制造云服务组合时必须考虑这种相关性。本文提出了一种关联感知制造云服务描述模型，用于描述单个服务对其他相关服务的QoS依赖性。在此基础上，提出了一种自动获取服务间相关QoS值的服务关联映射模型。此外，提出了一种基于遗传算法的关联感知最优服务选择的有效方法。一个案例研究表明，当考虑到这种相关性时，可以获得更高质量的服务组合。仿真实验验证了该方法的有效性和高效性。
Recently, cloud manufacturing has attracted much attention from both academic and industry communities. Manufacturing cloud service composition and optimization is critical to the optimal resources allocation in cloud manufacturing. Since there are many manufacturing cloud services available with similar functions but different quality of service (QoS), and with potential quality correlations among them, such correlations must to be considered for manufacturing cloud service composition. In this paper, a correlation-aware manufacturing cloud service description model is presented to characterize the QoS dependence of an individual service on other related services. Based on such a model, a service correlation mapping model is proposed for getting correlation QoS values among services automatically. In addition, an effective approach for the correlation-aware optimal service selection is proposed based on a genetic algorithm. A case study indicates that services composition of higher quality can be obtained when such correlations are considered. And the effectiveness and efficiency of the proposed approach are demonstrated via simulation studies.