A Markov chain-based multi-criteria framework for dynamic cloud service selection using user feedback

Cloud service selection is a critical decision that directly impacts an organization’s competitive position. Despite substantial research, a unified approach that integrates user feedback into the selection process remains underexplored. In response to this, we propose a novel multi-criteria decision-making (MCDM) framework designed to address this gap. The framework is structured into three key phases: (1) determining criteria weights, (2) generating an initial service ranking, and (3) refining the final ranking through Markov chain analysis. Our approach introduces an innovative multi-step consensus process that refines service rankings using established MCDM techniques. A unique feature of this framework is its integration of a Markov chain-based method to analyze and track shifts in user feedbacks by combining both current and historical feedback. This dynamic modeling ensures the rankings reflect evolving user experiences, offering a more accurate and real-time evaluation of cloud services. This comprehensive framework not only enables users to make personalized and informed service selections but also provides cloud service providers (CSPs) with actionable insights into market trends and customer needs. The effectiveness of the framework is validated through a case study using real-world data, demonstrating its robustness and practical value in enhancing decision-making.