No abstract

An increase in the number of cloud services makes service selection a challenging issue for cloud users. It is important to determine the best service that can fulfill user requirements. To this end, this paper proposes a hybrid multiple-attribute decision-making (MADM) model. The proposed method considers service measurement index cloud (SMICloud) structure for qualitative attributes of cloud services as well as user requirements based on fuzzy values to consider vague user requirements. Analytical hierarchy process (AHP) and fuzzy logic are used to rank cloud services. Furthermore, a fuzzy Delphi filtering method is proposed to decrease the execution time of ranking cloud services. In experiments, different aspects such as accuracy, execution time, scalability, and sensitivity analysis are investigated. The results confirm that the proposed method outperforms available methods in terms of execution time and scalability. Furthermore, the experiments show that the proposed method has achieved an accuracy of 96%.

Many cloud providers present various services with different attributes. It is a complex, lengthy process to select a cloud service that meets user requirements from an assortment of services. At the same time, user requirements are sometimes defined with imprecision (sets or intervals), whereas it is also important to consider the quality of user feedback (QoU) and quality of service (QoS) attributes for ranking. Besides, each MADM method has a different procedure, which causes functional contradictions. These contradictions have led to confusion in choosing the best MADM method. It is necessary to provide a method that harmonizes the results. Therefore, choosing a method for ranking cloud services that addresses these issues is currently a challenge. This paper proposes an optimal cloud service ranking (OCSR) method that ranks cloud services efficiently based on imprecise user requirements in both QoS and QoU aspects. OCSR consists of four stages including receiving the requirements, preprocessing, ranking, and integrating the ranking results. At the receiving requirements stage, the query format is created. In the preprocessing stage, a requirement interval is created for considering imprecise user requirements in order to filter inappropriate services. Based on QoS and QoU attributes, cloud services are then ranked through multiple multi-attribute decision-making (multi-MADM) methods such as the prominent MADM techniques. Finally, the ranking outputs of various methods are integrated to obtain the optimal results. The experimental results confirm that the OCSR outperforms the previous methods in terms of optimality of ranking, sensitivity analyses, and scalability.