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Cloud Service Evaluation and Selection Using Fuzzy Hybrid MCDM Approach in Marketplace

Cloud Service Evaluation and Selection Using Fuzzy Hybrid MCDM Approach in Marketplace

Thiruselvan Subramanian, Nickolas Savarimuthu
Copyright: © 2016 |Volume: 5 |Issue: 2 |Pages: 36
ISSN: 2156-177X|EISSN: 2156-1761|EISBN13: 9781466693210|DOI: 10.4018/IJFSA.2016040108
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MLA

Subramanian, Thiruselvan, and Nickolas Savarimuthu. "Cloud Service Evaluation and Selection Using Fuzzy Hybrid MCDM Approach in Marketplace." IJFSA vol.5, no.2 2016: pp.118-153. http://doi.org/10.4018/IJFSA.2016040108

APA

Subramanian, T. & Savarimuthu, N. (2016). Cloud Service Evaluation and Selection Using Fuzzy Hybrid MCDM Approach in Marketplace. International Journal of Fuzzy System Applications (IJFSA), 5(2), 118-153. http://doi.org/10.4018/IJFSA.2016040108

Chicago

Subramanian, Thiruselvan, and Nickolas Savarimuthu. "Cloud Service Evaluation and Selection Using Fuzzy Hybrid MCDM Approach in Marketplace," International Journal of Fuzzy System Applications (IJFSA) 5, no.2: 118-153. http://doi.org/10.4018/IJFSA.2016040108

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Abstract

Cloud services are offered independently or combining two or more services to satisfy consumer requirements. Different types of cloud service providers such as direct sellers, resellers and aggregators provide services with different level of service features and quality. The selection of best suitable services involves multi-criteria nature of services to be compared with the presence of both qualitative and quantitative factors, which make it considerably more complex. To overcome this complexity, a fuzzy hybrid multi-criteria decision making approach has been proposed, which includes both qualitative and quantitative factors. Triangular fuzzy numbers are used in all pairwise comparison matrices in the Fuzzy ANP and the criteria weights are utilized by Fuzzy TOPSIS and Fuzzy ELECTRE methods to rank the alternatives. This strategy is demonstrated with selection of cloud based collaboration tool for designers. Finally, sensitivity analysis is performed to prove the robustness of the proposed approach.

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