Multi-Attribute Decision-Making Methods to a Cloud Service Providing Selection

With the emergence of cloud computing in recent years, many cloud service providers offer various types of services to customers. There is a significant possibility for optimizing the selection of services to serve users as efficiently as possible. Indeed, cloud system providers (CSPs) offer a variety of services with different payment cost criteria. From the customer's point of view, the most important factor for choosing the appropriate CSP is service quality Service quality which is relevant to such different features as security, cost, reputation, finances, performance, etc. In this article, two multi-feature optimization methods, including the Analytical Hierarchical Process (AHP) and Shannon's entropy method are considered to rank different features of CSPs. The AHP method performs the ranking, by using an initial pairwise decision matrix proposed by some experts. But Shannon's entropy method corresponds to the maximum likelihood problem and is performed without the intervention experts' decisions. Therefore, comparing these methods determines the weaknesses and strengths of the experts' decisions. Next, the results obtained by these methods have been combined with the TOPSIS and COPRAS methods to analyze the data offered by users to rank some selected cloud service providers. The TOPSIS and COPRAS methods are performed based on clinging to positive and negative ideals. Besides that, the COPRAS method considers the superiority and dependence between features to suggest the final indicators' ranks.