Multi-tenancy is an approach to share one application instance among multiple customers by providing each of them a dedicated view. This approach is commonly used by SaaS providers to reduce the costs for service provisioning. Tenants also expect to be isolated in terms of the performance they observe and the providers inability to offer performance guarantees is a major obstacle for potential cloud customers. To guarantee an isolated performance it is essential to control the resources used by a tenant. This is a challenge, because the layers of the execution environment, responsible for controlling resource usage (e.g., operating system), normally do not have knowledge about entities defined at the application level and thus they cannot distinguish between different tenants. Furthermore, it is hard to predict how tenant requests propagate through the multiple layers of the execution environment down to the physical resource layer. The intended abstraction of the application from the resource controlling layers does not allow to solely solving this problem in the application. In this paper, we propose an approach which applies resource demand estimation techniques in combination with a request based admission control. The resource demand estimation is used to determine resource consumption information for individual requests. The admission control mechanism uses this knowledge to delay requests originating from tenants that exceed their allocated resource share. The proposed method is validated by a widely accepted benchmark showing its applicability in a setup motivated by today's platform environments.

Multi-tenancy is an approach to share one application instance among multiple customers by providing each of them a dedicated view. This approach is commonly used by SaaS providers to reduce the costs for service provisioning. Tenants also expect to be isolated in terms of the performance they observe and the providers inability to offer performance guarantees is a major obstacle for potential cloud customers. To guarantee an isolated performance it is essential to control the resources used by a tenant. This is a challenge, because the layers of the execution environment, responsible for controlling resource usage (e.g., operating system), normally do not have knowledge about entities defined at the application level and thus they cannot distinguish between different tenants. Furthermore, it is hard to predict how tenant requests propagate through the multiple layers of the execution environment down to the physical resource layer. The intended abstraction of the application from the resource controlling layers does not allow to solely solving this problem in the application. In this paper, we propose an approach which applies resource demand estimation techniques in combination with a request based admission control. The resource demand estimation is used to determine resource consumption information for individual requests. The admission control mechanism uses this knowledge to delay requests originating from tenants that exceed their allocated resource share. The proposed method is validated by a widely accepted benchmark showing its applicability in a setup motivated by today's platform environments.

Software-as-a-Service (SaaS) often shares one single application instance among different tenants to reduce costs. However, sharing potentially leads to undesired influence from one tenant onto the performance observed by the others. Furthermore, providing one tenant additional resources to support its increasing demands without increasing the performance of tenants who do not pay for it is a major challenge. The application intentionally does not manage hardware resources, and the OS is not aware of application level entities like tenants. Thus, it is difficult to control the performance of different tenants to keep them isolated. These problems gain importance as performance is one of the major obstacles for cloud customers. Existing work applies request based admission control mechanisms like a weighted round robin with an individual queue for each tenant to control the share guaranteed for a tenant. However, the computation of the concrete weights for such an admission control is still challenging. In this paper, we present a fitness function and optimization approach reflecting various requirements from this field to compute proper weights with the goal to ensure an isolated performance as foundation to scale on a tenants basis.

The cloud computing paradigm enables the provision of cost efficient IT-services by leveraging economies of scale and sharing data center resources efficiently among multiple independent applications and customers. However, the sharing of resources leads to possible interference between users and performance problems are one of the major obstacles for potential cloud customers. Consequently, it is one of the primary goals of cloud service providers to have different customers and their hosted applications isolated as much as possible in terms of the performance they observe. To make different offerings, comparable with regards to their performance isolation capabilities, a representative metric is needed to quantify the level of performance isolation in cloud environments. Such a metric should allow to measure externally by running benchmarks from the outside treating the cloud as a black box. In this article, we propose three different types of novel metrics for quantifying the performance isolation of cloud-based systems. We consider four new approaches to achieve performance isolation in Software-as-a-Service (SaaS) offerings and evaluate them based on the proposed metrics as part of a simulation-based case study. To demonstrate the effectiveness and practical applicability of the proposed metrics for quantifying the performance isolation in various scenarios, we present a second case study evaluating performance isolation of the hypervisor Xen.