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A Multi Criteria Decision Making Method for Cloud Service Selection and Ranking

A Multi Criteria Decision Making Method for Cloud Service Selection and Ranking

Rakesh Ranjan Kumar, Chiranjeev Kumar
Copyright: © 2018 |Volume: 9 |Issue: 3 |Pages: 14
ISSN: 1941-6237|EISSN: 1941-6245|EISBN13: 9781522543541|DOI: 10.4018/IJACI.2018070101
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MLA

Kumar, Rakesh Ranjan, and Chiranjeev Kumar. "A Multi Criteria Decision Making Method for Cloud Service Selection and Ranking." IJACI vol.9, no.3 2018: pp.1-14. http://doi.org/10.4018/IJACI.2018070101

APA

Kumar, R. R. & Kumar, C. (2018). A Multi Criteria Decision Making Method for Cloud Service Selection and Ranking. International Journal of Ambient Computing and Intelligence (IJACI), 9(3), 1-14. http://doi.org/10.4018/IJACI.2018070101

Chicago

Kumar, Rakesh Ranjan, and Chiranjeev Kumar. "A Multi Criteria Decision Making Method for Cloud Service Selection and Ranking," International Journal of Ambient Computing and Intelligence (IJACI) 9, no.3: 1-14. http://doi.org/10.4018/IJACI.2018070101

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Abstract

This article describes how with the rapid growth of cloud services in recent years, it is very difficult to choose the most suitable cloud services among those services that provide similar functionality. The quality of services (QoS) is considered the most significant factor for appropriate service selection and user satisfaction in cloud computing. However, with a vast diversity in the cloud services, selection of a suitable cloud service is a very challenging task for a customer under an unpredictable environment. Due to the multidimensional attributes of QoS, cloud service selection problems are treated as a multiple criteria decision-making (MCDM) problem. This study introduces a methodology for determining the appropriate cloud service by integrating the AHP weighing method with TOPSIS method. Using AHP, the authors define the architecture for selection process of cloud services and compute the criteria weights using pairwise comparison. Thereafter, with the TOPSIS method, the authors obtain the final ranking of the cloud service based on overall performance. A real-time cloud case study affirms the potential of our proposed methodology, when compared to other MCDM methods. Finally, a sensitivity analysis testifies the effectiveness and the robustness of our proposed methodology.

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