A decision-making approach based on fuzzy AHP-TOPSIS methodology for selecting the appropriate cloud solution to manage big data projects

The objective of this paper is to propose a hybrid decision-making methodology based on affinity diagram, fuzzy analytic hierarchy process (FAHP) and fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) to evaluate, rank and select the most appropriate cloud solutions to accommodate and manage big data projects. In fact, the strategic priority of many corporations consists in the creation of competitive advantages by using new available technologies, processes and governance mechanisms, such as big data and cloud computing. Since the technology is permanently subject to advances and developments, the question for many businesses is how to benefit from big data using the power of technical flexibility that cloud computing can provide. In this context, selecting the most adequate cloud solution to host big data projects is a complex issue that requires an extensive evaluation process. Thus, to assist users to efficiently select their most preferred cloud solution, we propose a hybrid decision-making methodology that meets these requirements in four stages. In the first stage, the identification of evaluation criteria is performed by a decision-making committee using Affinity Diagram. Due to the varied importance of the selected criteria, a FAHP process is used in the second stage to assign the importance weights for each criterion, while FTOPSIS process, in the third stage, employs these weighted criteria as inputs to evaluate and measure the performance of each alternative. In the last step, a sensitivity analysis is performed to evaluate the impact of criteria weights on the final rankings of alternatives.