ClouDSS: A decision support system for cloud service selection

Date

2017-01-01

Author

Şener, Umut
Gökalp, Ebru
Eren, Pekin Erhan

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Cloud computing brings in significant technical advantages and enables companies, especially small and medium size enterprises (SMEs), to eliminate up-front capital expenditures. This is due to the various benefits it provides, such as pay-as-you-go service model, flexibility of services, and on-demand accessibility. The proliferation of cloud services leads to their wide spread use and calls for comprehensive evaluation approaches in order to be able to choose the most suitable alternatives. To this end, existing studies in the literature generally provide solutions incorporating a single method for making such decisions. Therefore, this study proposes a more comprehensive solution in the form of a decision support system named as ClouDSS which employs various Multi-Criteria Decision Making (MCDM) methods with the aim of optimizing cloud service selection decisions. ClouDSS has a default decision model, which can be customized according to enterprise-specific requirements, for evaluating the suitability of cloud services with respect to business needs. After presenting the main components of ClouDSS, the employed cloud service selection process is described in order to highlight the associated tasks, including both objective and subjective evaluation approaches. Furthermore, the applicability of the proposed system is demonstrated through a case study.

Subject Keywords

Economics of cloud computing , Service selection , Decision support system , SME , Multi-criteria decision making

URI

https://hdl.handle.net/11511/30895

DOI

https://doi.org/10.1007/978-3-319-68066-8_19

Collections

Graduate School of Informatics, Conference / Seminar

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Citation Formats

U. Şener, E. Gökalp, and P. E. Eren, “ClouDSS: A decision support system for cloud service selection,” 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/30895.