A Multi-Faceted Approach to Cloud Provider Selection: Combining PROMETHEE, Fuzzy-AHP, and SLA Insights

Abstract

The selection of a Cloud Service Provider (CSP) is a critical decision for organizations that are 
looking to adopt scalable, secure, and cost-efficient cloud adoption. The complexity of CSP 
selection arises from multiple competing factors, including cost, performance, security, 
regulatory compliance, and service reliability. Traditional decision-making models often fail 
to address uncertainty and dynamic business requirements. To overcome these shortcomings, 
this research integrates PROMETHEE, Fuzzy Analytical Hierarchy Process (Fuzzy-AHP), and 
Service Level Agreement (SLA) analysis to build a multi-criteria decision-making framework 
for the optimization of selecting a CSP. The proposed model will rely on Fuzzy-AHP for 
handling subjective decision-making and uncertainty, PROMETHEE for ranking CSPs 
according to predefined multi-criteria preferences, and SLA analysis to ensure that enforceable 
service expectations such as uptime and performance are met. This hybrid framework will 
allow organizations to make structured, transparent, and data-driven decisions tailored to their 
unique cloud adoption needs. Results show that the proposed model has a better cost efficiency 
of 91%, 94% service reliability, and a task completion time of 115ms, surpassing the traditional 
CSP selection methods. This confirms the model's effectiveness in enhancing decision 
confidence, optimizing resource allocation, and improving cloud investment strategies. Further 
research can be done by integrating AI-driven predictive analytics and blockchain-based SLA 
enforcement to improve CSP evaluation while ensuring security, accountability, and 
adaptability in evolving cloud environments.