Title :
Composable correlation mining of cloud service in cloud manufacturing
Author :
Guo, Hua ; Zhang, Lin ; Tao, Fei ; Ren, Zhiyun ; Luo, Yongliang
Author_Institution :
Sch. of Autom. Sci. & Electr. Eng., Beihang Univ., Beijing, China
Abstract :
The emergence of cloud manufacturing (CMfg) provides a new opportunity for the change of manufacturing towards service-oriented model. Cloud service composition (CSC), which can realize the added value of cloud service (CS), is the core to implement CMfg. Since there always exist correlations among CSs, especially composable correlation (CoC), which can affect the construction of CSC path. Hence, how to mine the CoC among CSs and judge which kind of CoC between them is a key issue. This paper presents the formalized description for CoC, and designs decision algorithms to judge CoCs between CSs based on bipartite graph. The case study illustrates the application of proposed algorithms.
Keywords :
cloud computing; graph theory; service-oriented architecture; bipartite graph; cloud manufacturing; cloud service composition; composable correlation mining; decision algorithm; service-oriented model; Bipartite graph; Cascading style sheets; Correlation; Manganese; Optimal matching; Silicon; Transforms; Composabale correlation; cloud manufacturing (CMfg); cloud service composition; mining algorithm;
Conference_Titel :
Industrial Engineering and Engineering Management (IEEM), 2011 IEEE International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4577-0740-7
Electronic_ISBN :
2157-3611
DOI :
10.1109/IEEM.2011.6118247