Cloud-FuSeR: Fuzzy ontology and MCDM based cloud service selection

• Discuss why Cloud service selection problem is important.
• Design a fuzzy ontology for Cloud service selection problems.
• Identify proper time points and approaches for operating fuzzy variables.
• Consider the functional similarity and QoS performance simultaneously by distinguishing compositions of service functions.
• Combine user preferences and expert perceptions on service functions and their evaluation properties.

With the rapidly growing number of available Cloud services, to fulfill the need for ordinary users to select accurate services has become a significant challenge. However, as a Cloud service environment encompasses many uncertainties that may hinder users to make sound decisions, it is highly desirable to handle fuzzy information when choosing a suitable service in an uncertain environment. In this paper, we present a novel fuzzy decision-making framework that improves the existing Cloud service selection techniques. In particular, we build a fuzzy ontology to model uncertain relationships between objects in databases for service matching, and present a novel analytic hierarchy process approach to calculate the semantic similarity between concepts. We also present a multi-criteria decision-making technique to rank Cloud services. Furthermore, we conduct extensive experiments to evaluate the performance of the fuzzy ontology-based similarity matching. The experimental results show the efficiency of the proposed method.