Prioritizing the solution of cloud service selection using integrated MCDM methods under Fuzzy environment

Abstract

Cloud service selection plays a crucial role in terms of on-demand service selection on a subscription basis. As a result of wide-range availability of cloud services with similar functionalities, it is very crucial to determine which service best addresses the user’s desires and objectives. This paper aims to design a new cloud service selection model under the fuzzy environment by utilizing the analytical hierarchy process (AHP) and fuzzy technique for order preference by similarity to ideal solution (TOPSIS). The AHP method is enforced to configure the structure of cloud service selection problem and to impel the criteria weight using the pairwise comparisons, and the TOPSIS method utilizes the final ranking of the solution. In our proposed model, the non-functional quality of service requirements is taken into consideration for selecting appropriate service. Furthermore, the proposed model exploits a set of pre-defined linguistic variables, parameterized by triangular fuzzy numbers for evaluating each criteria weights. The experimental results obtained using the real-time cloud service domains prove the efficacy of our proposed model and demonstrate the effectiveness by inducing better performance, when compared against other available cloud service selection algorithms. Finally, the sensitivity analysis is persuaded to confirm the robustness of our proposed model.