• Propose a time-aware service selection approach for uncertain cloud industry. • Formulate a multi-criterion decision-making problem using interval neutrosophic set. • Support tradeoffs between performance-costs and potential risks in time periods. • Establish the CINS theory to calculate and compare the candidate cloud services. • Develop a CINS ranking method to create a ranked list of trustworthy cloud services.  a r t i c l e i n f o Cloud services consumers face a critical challenge in selecting trustworthy services from abundant candidates, and facilitating these choices has become a critical issue in the uncertain cloud industry. This paper employs the time series analysis to address challenges resulting from fluctuating quality of service, flexible service pricing and complicated potential risks in order to propose a time-aware trustworthy service selection approach with tradeoffs between performance-costs and potential risks. The original evaluation data about the services is preprocessed using a cloud model, and interval neutrosophic set (INS) theory is utilized to describe and measure the performance-costs and potential risks of services. In order to calculate and compare the candidate services while supporting tradeoffs between performance-costs and potential risks in different time periods, we established a cloud service interval neutrosophic set (CINS) and designed its operators and calculation rules, with theoretical proofs provided. The problem of time-aware trustworthy service selection is formulated as a multi-criterion decision-making (MCDM) problem of creating a ranked services list using CINS, and it is solved by developing a CINS ranking method. Finally, experiments based on a real-world dataset illustrate the practicality and effectiveness of the proposed approach.
Ma et al. REF propose a time-aware service selection approach by using interval neutrosophic set.
Toward trustworthy cloud service selection : A time-aware approach using interval neutrosophic set
J. Parallel Distributed Comput.
J. Parallel Distributed Comput.
Computer Science