Title: Multiple phases-based classifications for cloud services

Authors: Abdullah Ali; Siti Mariyam Shamsuddin; Fathy E. Eassa; Faisal Saeed

Addresses: UTM Big Data Centre, Ibnu Sina Institute for Scientific and Industrial Research, Universiti Teknologi Malaysia, 81310 Johor, Malaysia; Faculty of Computing, Universiti Teknologi Malaysia, 81310 Johor, Malaysia ' UTM Big Data Centre, Ibnu Sina Institute for Scientific and Industrial Research, Universiti Teknologi Malaysia, 81310 Johor, Malaysia; Faculty of Computing, Universiti Teknologi Malaysia, 81310 Johor, Malaysia ' Department of Computer Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia ' Faculty of Computing, Universiti Teknologi Malaysia, 81310 Johor, Malaysia

Abstract: The current problem in cloud services discovery is the lack of standardisation in the naming convention and the heterogeneous type of its features. Therefore, to accurately retrieve the appropriate services, an intelligent service discovery is required. To do that, the cloud services attributes should be extracted from the heterogeneous formats and represented it in a uniform manner such as ontology to increase the accuracy of discovery. The extraction process can be done by classifying the cloud services into different types. In this paper, single and multiple phases-based classifications are performed using support vector machine (SVM) and naïve Bayes as classifiers. The Cloud Armor's dataset used which represents four classes of cloud services. Topic modelling using MALLET tool is used for dataset pre-processing. The experimental results showed that the classification accuracy for the two phases-based and single phase-based classifications reached 87.90% and 92.78% respectively.

Keywords: cloud computing; cloud services; classification; support vector machine; SVM; naïve Bayes; features selection; topic modelling; cloud services discovery; pre-processing; standard deviation.

DOI: 10.1504/IJCAET.2018.092833

International Journal of Computer Aided Engineering and Technology, 2018 Vol.10 No.4, pp.341 - 354

Received: 03 Jul 2015
Accepted: 09 Nov 2015
Published online: 01 Jul 2018 *

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