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The Scientific World Journal
Volume 2014, Article ID 459375, 10 pages
http://dx.doi.org/10.1155/2014/459375
Review Article

Cloud Service Selection Using Multicriteria Decision Analysis

1Faculty of Computer Science & Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia
2School of Electrical Engineering and Computer Science, The University of Newcastle, Callaghan, NSW 2308, Australia
3Department of Computing Science, University of Alberta, Edmonton, AB, Canada P6G 2M7

Received 11 October 2013; Accepted 19 December 2013; Published 13 February 2014

Academic Editors: A. N. Dudin and A. Kaloxylos

Copyright © 2014 Md Whaiduzzaman et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Cloud computing (CC) has recently been receiving tremendous attention from the IT industry and academic researchers. CC leverages its unique services to cloud customers in a pay-as-you-go, anytime, anywhere manner. Cloud services provide dynamically scalable services through the Internet on demand. Therefore, service provisioning plays a key role in CC. The cloud customer must be able to select appropriate services according to his or her needs. Several approaches have been proposed to solve the service selection problem, including multicriteria decision analysis (MCDA). MCDA enables the user to choose from among a number of available choices. In this paper, we analyze the application of MCDA to service selection in CC. We identify and synthesize several MCDA techniques and provide a comprehensive analysis of this technology for general readers. In addition, we present a taxonomy derived from a survey of the current literature. Finally, we highlight several state-of-the-art practical aspects of MCDA implementation in cloud computing service selection. The contributions of this study are four-fold: (a) focusing on the state-of-the-art MCDA techniques, (b) highlighting the comparative analysis and suitability of several MCDA methods, (c) presenting a taxonomy through extensive literature review, and (d) analyzing and summarizing the cloud computing service selections in different scenarios.