An MCDM Approach for Cloud Computing Service Selection Based on Best-Only Method

Abstract

Owing to the significant and continuous development of cloud computing technology and the increasing number of cloud service providers (CSPs) that have emerged, CSP selection has become a challenging decision for many organizations. To accurately evaluate the services provided by various CSPs, many independent criteria must be considered, resulting in a multi-criteria decision-making (MCDM) problem. In MCDM problems, several alternatives are assessed against several criteria to identify the optimal alternative (s). The challenges stem from complex computation as well as less consistency, resulting in less reliable results. In this paper, a new method, called the best-only method (BOM), is proposed for solving the CSP selection problem, which is viable, efficient, and fully consistent. The proposed method was evaluated and validated using a use-case scenario, demonstrating its efficiency and appropriateness. The proposed method is compared with two popular MCDM methods, analytical hierarchical process (AHP) and best-worst method (BWM), from three perspectives: efficiency, consistency ratio (CR), and total deviation (TD). Based on the obtained results, AHP and BWM require 75.3% and 46.2% more comparisons, respectively, than the proposed method. The consistency ratios for AHP, BWM, and the proposed method were 37.92%, 13.35%, and 0%, respectively. The total deviations are 21.26, 8.65, and 0 for AHP, BWM, and the proposed method, respectively. These results clearly indicate that the proposed method outperforms AHP and BWM in terms of computing complexity and consistency, making it more efficient and reliable.