Abstract
Because the wide use of cloud computing has led to vast amounts of service information in the network, the quick identification and the automatic classification of cloud services have become the key to the quick and accurate location of the expected service.
In this paper, a new method of automatic classification of cloud services based on weighted Euclidean distance is proposed. Firstly, we perform the service pretreatment on the collected dataset, and extract features to build a text model based on VSM (Vector Space Model). Further on, a new algorithm named WT_K-means algorithm is proposed by improving the distance function of the original K-means algorithm. Experiments have been carried out under two-dimensional test set and real service dataset, respectively. The results show that the pretreatment of service can abstract the functional characteristics of the original WSDL (Web Service Description Language) documents, and the proposed WT_K-means algorithm can effectively classify services according to these characteristics.
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Lou, Y., Zhuang, Y., Huo, Y. (2016). Automatic Classification of Cloud Service Based on Weighted Euclidean Distance. In: Sun, X., Liu, A., Chao, HC., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2016. Lecture Notes in Computer Science(), vol 10039. Springer, Cham. https://doi.org/10.1007/978-3-319-48671-0_21
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DOI: https://doi.org/10.1007/978-3-319-48671-0_21
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