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.

When creating a performance model, it is necessary to quantify the amount of resources consumed by an application serving individual requests. In distributed enterprise systems, these resource demands usually cannot be observed directly, their estimation is a major challenge. Different statistical approaches to resource demand estimation based on monitoring data have been proposed, e.g., using linear regression or Kalman filtering techniques. In this paper, we present LibReDE, a library of ready-to-use implementations of approaches to resource demand estimation that can be used for online and offline analysis. It is the first publicly available tool for this task and aims at supporting performance engineers during performance model construction. The library enables the quick comparison of the estimation accuracy of different approaches in a given context and thus helps to select an optimal one.

Virtualization increases the complexity and dynamics of modern software architectures making it a major challenge to manage the end-to-end performance of applications. Architecture-level performance models can help here as they provide the modeling power and analysis fexibility to predict the performance behavior of applications under varying workloads and configurations. However, the construction of such models is a complex and time-consuming task. In this position paper, we discuss how the existing concept of virtual appliances can be extended to automate the extraction of architecture-level performance models during system operation.

Queueing Petri nets are a powerful formalism that can be exploited for modeling distributed systems and analyzing their performance and scalability. By combining the modeling power and expressiveness of queueing networks and stochastic Petri nets, queueing Petri nets provide a number of advantages. In this paper, we present our tool QPME (Queueing Petri net Modeling Environment) for modeling and analysis using queueing Petri nets. QPME provides an Eclipse-based editor for building queueing Petri net models and a powerful simulation engine for analyzing these models. The development of the tool started in 2003 and since then the tool has been distributed to more than 120 organizations worldwide.

With the increasing adoption of virtualization and the transition towards Cloud Computing platforms, modern business information systems are becoming increasingly complex and dynamic. This raises the challenge of guaranteeing system performance and scalability while at the same time ensuring efficient resource usage. In this paper, we present a historical perspective on the evolution of model-based performance engineering techniques for business information systems focusing on the major developments over the past several decades that have shaped the field. We survey the state-of-the-art on performance modeling and management approaches discussing the ongoing efforts in the community to increasingly bridge the gap between high-level business services and low level performance models. Finally, we wrap up with an outlook on the emergence of self-aware systems engineering as a new research area at the intersection of several computer science disciplines.

Queueing Petri nets are a powerful formalism that can be exploited for modeling distributed systems and analyzing their performance and scalability. By combining the modeling power and expressiveness of queueing networks and stochastic Petri nets, queueing Petri nets provide a number of advantages. In this paper, we present Version 2.0 of our tool QPME (Queueing Petri net Modeling Environment) for modeling and analysis of systems using queueing Petri nets. The development of the tool was initiated by Samuel Kounev in 2003 at the Technische Universitä Darmstadt in the group of Prof. Alejandro Buchmann. Since then the tool has been distributed to more than 100 organizations worldwide. QPME provides an Eclipse-based editor for building queueing Petri net models and a powerful simulation engine for analyzing the models. After presenting the tool, we discuss ongoing work on the QPME project and the planned future enhancements of the tool.