An Uncertainty-aware Integrated Fuzzy AHP-WASPAS Model to Evaluate Public Cloud Computing Services

Cloud service evaluation and selection has emerged as a significant challenge for consumers and organizational decision makers due to the proliferation and exponential growth of cloud services over the internet. The plethora of cloud service providers (CSPs) with a range of functionally similar public cloud services, makes it difficult for the service consumers to select the appropriate and optimal cloud services. Several evaluation models have been proposed in the past few years. However, existing literature lacks a systematic procedure aggregating the user feedback and real-world performance assessment data, while incorporating the uncertainty involved in the subjective human preferences. In addition, existing literature reports constrained evaluation criteria while neglecting several decisive factors. In this study, we formulate this problem as a Multi-Criteria Decision making (MCDM) problem and develop an integrated decision model based on Fuzzy Analytic Hierarchy Process (FAHP) and Weighted Aggregated Sum-Product Assessment (WASPAS) methods. A comprehensive and multi-dimensional hierarchical model is structured which comprises of 9 primary and 30 secondary evaluation factors. Relative weight assessment of the primary and secondary evaluation factors, is performed through FAHP with extent analysis and WASPAS is used to rank the candidate cloud services. A case study on six real-world Public cloud services is performed to demonstrate the efficacy of the proposed model. Finally, the robustness of the proposed model is validated through sensitivity analysis. (C) 2018 The Authors. Published by Elsevier B.V.