Choosing a cloud service provider with a multi-criteria approach for small, medium and large-sized companies

Today, businesses place great importance on cloud computing (CC) services to gain a competitive advantage and increase operational efficiency. The correct selection of cloud computing service providers (CCSP) plays a decisive role in business success in terms of critical factors such as service, security, cost, and CPU. This study was conducted using multi-criteria decision-making (MCDM) methods to assist small, medium, and large-scale businesses in selecting CCSPs. The methods used in this study were based on data obtained from experienced and expert individuals in the field. As a result of the obtained data, a matrix consisting of 7 different alternatives (CCSPs), 4 main criteria, and 13 sub-criteria was created, and the study was conducted based on this matrix. In this thesis, MCDM methods such as SWARA, TOPSIS, VIKOR, and Borda Count were used to determine the most suitable CCSP. The SWARA method was used to determine the criteria weights, the TOPSIS method compared the alternatives with the ideal solution, and the VIKOR method identified the compromise solution. The Borda Count method was added to combine the results obtained from other methods and determine a final CCSP alternative. The analysis results showed that CPU performance had the highest importance among the main criteria, while storage and data center location were the most important factors among the sub-criteria. Due to the different alternatives obtained through SWARA, TOPSIS, and VIKOR methods, the Borda Count method was also included, and a final CCSP alternative was determined. As a result, Amazon was identified as the best CCSP through the Borda Count method. These findings indicate that different decision-making methods can yield different results in the CCSP selection process. Therefore, businesses need to choose the most appropriate method according to their needs and priorities. The study aims to emphasize the importance of criteria and decision-making methods used in CCSP selection and contribute to more informed decisions in this area.