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

Abstract

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.