No abstract

Application-level multi-tenancy is an architectural design principle for Software-as-a-Service applications to enable the hosting of multiple customers (or tenants) by a single application instance. Despite the operational cost and maintenance benefits of application-level multitenancy, the current middleware component models for multi-tenant application design are inflexible with respect to providing different software variations to different customers. In this paper we show that this limitation can be solved by a multitenancy support layer that combines dependency injection with middleware support for tenant data isolation. Dependency injection enables injecting different software variations on a per tenant basis, while dedicated middleware support facilitates the separation of data and configuration metadata between tenants. We implemented a prototype on top of Google App Engine and we evaluated by means of a case study that the improved flexibility of our approach has little impact on operational costs and upfront application engineering costs.

Multi-tenancy has shown promising results in achieving high operational cost efficiency by sharing hardware and software resources among multiple customer organisations, called tenants. In the context of cloud computing, this paradigm enables cloud providers to reduce operational costs by dividing resources and to simplify application management and maintenance. Maximum cost efficiency is achieved with application-level multi-tenancy. However, this high level of resource sharing complicates performance isolation between the different tenants, i.e. ensuring compliance with the SLAs of the different tenants and ensuring that the behaviour of one tenant cannot adversely affect the performance of the other tenants.This paper explores the challenges of performance isolation in the context of multi-tenant SaaS applications. In addition, we propose a middleware architecture to enforce performance isolation based on the tenant-specific SLAs, using a tenant-aware profiler and a scheduler. Our prototype reveals promising initial results.

Cloud computing technologies can be used to more flexibly provision application resources. By exploiting multi-tenancy, instances can be shared between users, lowering the cost of providing applications. A weakness of current cloud offerings however, is the difficulty of creating customizable applications that retain these advantages. In this article, we define a feature-based cloud resource management model, making use of Software Product Line Engineering (SPLE) techniques, where applications are composed of feature instances using a service-oriented architecture. We focus on how resources can be allocated in a cost-effective way within this model, a problem which we refer to as the feature placement problem. A formal description of this problem, that can be used to allocate resources in a cost-effective way, is provided. We take both the cost of failure to place features, and the cost of using servers into account, making it possible to take energy costs or the cost of public cloud infrastructure into consideration during the placement calculation. Four algorithms that can be used to solve the feature placement problem are defined. We evaluate the algorithm solutions, comparing them with the optimal solution determined using an integer linear problem solver, and evaluating the execution times of the algorithms, making use of both generated inputs and a use case based on three applications. We show that, using our approach a higher degree of multi-tenancy can be achieved, and that for the considered scenarios, taking the relationships between features into account and using application-oriented placement performs 25% to 40% better than a purely feature-oriented placement.