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

Rakesh Ranjan Kumar and Chiranjeev Kumar
Additional contact information
Rakesh Ranjan Kumar: Department of Computer Science and Engineering, IIT (ISM), Dhanbad, India
Chiranjeev Kumar: Department of Computer Science and Engineering, IIT (ISM), Dhanbad, India

International Journal of Ambient Computing and Intelligence (IJACI), 2018, vol. 9, issue 3, 1-14

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.

Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/IJACI.2018070101 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jaci00:v:9:y:2018:i:3:p:1-14

Access Statistics for this article

International Journal of Ambient Computing and Intelligence (IJACI) is currently edited by Nilanjan Dey

More articles in International Journal of Ambient Computing and Intelligence (IJACI) from IGI Global
Bibliographic data for series maintained by Journal Editor ().