LAUSR

Cloud Computing has become a reliable solution for outsourcing business data and operation with its cost-effective and resource-efficient services. A key part of the success of the cloud is the multi-tenancy architecture, where a single instance of a service can be shared between a large number of users, also known as tenants. Service selection for multiple tenants presents a real challenge that has not been properly addressed in the literature so far. Most of the existing cloud services selection approaches are designed for a single-user, and hence are inefficient when applied to the case of a large group of users with different, and often, conflicting requirements. In this paper, we propose a multi-tenant cloud services evaluation framework, whereby service selection is carried out per group of tenants that can belong to different service classes, rather than per a single user. We formulate the cloud services selection for multi-tenants as a complex multi-attribute large-group decision-making (CMALGDM) problem. Skyline method is initially applied to reduce the search space by eliminating the dominated services regardless of tenants’ requirements. Tenants are clustered based on their profiles characterized by different personal, service, and environmental features. Each tenant is assigned a weight to reflect its importance in the decision-making. The weight of a tenant is determined locally based on its closeness to the group decision and globally by combining its local weight with its corresponding cluster weight to reflect its total contribution to the overall decision-making. The final ranking of the alternatives is guided by a dynamic consensus process to reach a final solution with the highest level of agreement. The proposed framework supports multiple types of information, including deterministic data, interval numbers, and fuzzy numbers, to realistically represent the heterogeneity and uncertainty of security information.