Cloud service scrutinization and selection framework (C3SF): A novel unified approach to cloud service selection with consensus

Abstract

Cloud service selection (CSS) remains a strategically significant decision and has a substantial impact on an organization’s competitive edge. Despite considerable research, the literature lacks a comprehensive unified approach to consensual CSS. Recognizing the significance of CSS decisions, in this paper, we propose a novel Cloud Service Scrutinization and Selection Framework (C3SF) that includes four interdependent phases: (1) requirements elicitation, (2) scrutinization, (3) evaluation, and (4) ranking & selection. As part of C3SF, we use conjunctive screening to scrutinize cloud services. We also propose a novel multi-criteria decision-making (MCDM) approach called the modified Best-Worst Method (MBWM), which computes the weights of criteria using early-stage consensus among decision-makers. In addition, we introduce an innovative two-step consensus process for ranking services using leading MCDM methods followed by an aggregation of ranks using a Markov chain-based approach. Moreover, to develop a broader consensus, we propose another two-stage novel mechanism comprising multi-aggregation and synthesis/fusion of rank information using a partially ordered set. We validate the performance and effectiveness of C3SF through a CSS case study using real-world data followed by a comprehensive analysis. The results show that C3SF is robust, practical, and suitable for well-informed decision-making.