Characterizing the performance of tenant data management in multi-tenant cloud authorization systems

Multi-tenancy leads to improved efficiency, improved scalability, and lower costs. With the recent evolution of Cloud Computing and Software-as-a-Service (SaaS) in particular, a flexible and scalable multi-tenant architecture is becoming highly important. In multi-tenant applications, each tenant has its own users and administrators and tenants even tend to be divided into multiple subtenants. As the number of tenants grows, the number of users and amount of data grows, thus a scalable architecture for the access control system is needed. The question arises how to distribute the users and data over multiple database instances. In this paper we present a hierarchical data management approach, taking performance metrics into account, for structuring the storage of tenant data in large multi-tenant environments. We introduce a logical representation of the tenants, the tenant tree, and make a mapping to the physical storage by introducing three models for load-balancing. Next, we focus on how to efficiently locate the required data and introduce multiple search approaches. We characterize the impact on the performance both theoretically and experimentally. Experiments confirm that the theoretical analysis is in line with the experimental results. When the amount of data increases significantly, dividing the data over multiple datastores in an efficient way will eliminate the overhead and lead to a performance gain, especially if most of the data is located at the leaf nodes of the tenant tree.status: publishe