Design and evaluation of a hierarchical multi-tenant data management framework for cloud applications

Cloud computing is a technology that enables elastic, on-demand resource provisioning. Migrating applications to the cloud can increase their elasticity, allowing them to adapt to workload changes by dynamically allocating resources. In a multi-tenant application multiple client organizations, each referred to as tenants, make use of one or more shared application instances. These shared instances must however behave like a private instance by guaranteeing both data separation and performance isolation for every tenant. In order to achieve high scalability, a multi-tenant application running on the elastic cloud requires a flexible and scalable architecture for both the computational resources and the storage resources. In this paper we present and evaluate the design of a data management framework which can be used to extend existing multi-tenant cloud applications in order to achieve high scalability of the storage resources. We describe the most important components, and discuss important design choices. The framework invokes data allocation algorithms in order to find a feasible allocation of tenant data resulting in a minimal operating cost and a maximal performance, while taking no more than 10 ms to execute

Design of a hierarchical software-defined storage system for data-intensive multi-tenant cloud applications

Software-Defined Storage (SDS) is an evolving concept in which the management and provisioning of data storage is decoupled from the physical storage hardware. Data-intensive multi-tenant SaaS applications running on the public cloud could benefit from the concepts introduced by SDS by managing the allocation of tenant data from the tenant's perspective, taking custom tenant policies and preferences into account. In this paper, we propose the design of a scalable multi-tenant SDS system. In our approach, tenants are hierarchically clustered based on multiple scenario-specific characteristics. The storage elasticity component of the SDS system is responsible for the dynamic (re-) allocation of tenant data over the available storage resources. It invokes the Hierarchical Bin Packing algorithm introduced in this paper to determine an optimized distribution of tenant data based on the hierarchical tenant tree. We evaluate our system by means of two case studies based on real-life data sets. Experiments confirm that the Hierarchical Bin Packing algorithm achieves a good performance, with execution times below 100 ms to calculate the allocation for 1000 tenants in a worst-case scenario. Furthermore, our system achieves an average utilization of the storage resources close to the configured allocation factor, with reallocation of tenant data balanced over time