Scalable user data management in multi-tenant cloud environments

Abstract

Abstract-The rise of cloud computing and its elastic, ondemand resource provisioning introduces the need for a flexible and scalable multi-tenant architecture. In a multi-tenant application every tenant (client) makes use of shared application instances, but each tenant typically has its own user data. The shared application instance behaves like a private instance by guaranteeing both data separation and performance separation for every tenant. As the number of tenants increases, the amount of data grows. A scalable solution for the storage is needed, allowing tenant data to be divided over multiple database instances, but taking into account performance isolation and custom data assurance policies.In this paper we introduce an abstraction layer for achieving high scalability for the storage of tenant data. This layer uses data allocation algorithms to determine an acceptable allocation of tenant data to different databases. We describe a mathematical model for the allocation of tenant data which can be optimized using existing linear programming techniques, and introduce the BDAA-n and FDAA, two algorithms that will find an optimal allocation of data by iterating over the possible permutations. The proposed solutions are evaluated based on their flexibility, complexity and efficiency. The flexibility of the BDAA and FDAA makes them easy to customize and extend to fit most scenarios, but the algorithms will achieve best results for tenants with a limited number of subtenants. Linear programming is an alternative for tenants with a higher number of subtenants, but the customizability of the algorithm for specific use cases is limited due to the need for linear functions.

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

“…The work presented in this paper extends previous work as described in [1], [2]. In [1] we introduced a hierarchical method for organizing tenants and storage of tenant data, and characterized the impact on the performance in a theoretical way.…”

“…These algorithms are either based on linear programming or permutation based and are summarized in table I. All algorithms are designed to work for a single tenant and its k subtenants (two levels of the tenant tree), but they can be applied on the full tenant tree in a recursive way as illustrated in [2].…”

“…In [1] we introduced a hierarchical method for organizing tenants and storage of tenant data, and characterized the impact on the performance in a theoretical way. In [2], we presented multiple data allocation algorithms for finding an acceptable allocation of tenant data to different database instances. In this paper we focus on the design of a data management framework which is built on top of an optimized version of one of the previously presented data allocation algorithms.…”

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

“…In order to retrieve the application data, the application server connects to the storage pool where the tenant data is stored (3). To achieve high scalability of the computational resources, the computational elasticity component monitors the current load on the provisioned application server instances (4). As the load increases, additional instances will be provisioned.…”

“…In previous work [4], [5], we worked on 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 database layer. This database layer consists of multiple relational databases, and the framework manages the distribution and retrieval of tenant data over the available instances, but guaranteeing the correct functioning of the data queries.…”