Capacity planning and performance modeling: from mainframes to client-server systems
Capacity planning and performance modeling: from mainframes to client-server systems
Trace-Based Load Characterization for Generating Performance Software Models
IEEE Transactions on Software Engineering
WOSP '02 Proceedings of the 3rd international workshop on Software and performance
Complete instrumentation requirements for performance analysis of Web based technologies
ISPASS '03 Proceedings of the 2003 IEEE International Symposium on Performance Analysis of Systems and Software
Journal of Systems and Software
Performance Modeling and Evaluation of Distributed Component-Based Systems Using Queueing Petri Nets
IEEE Transactions on Software Engineering
Toward the Reverse Engineering of UML Sequence Diagrams for Distributed Java Software
IEEE Transactions on Software Engineering
Performance Model Estimation and Tracking Using Optimal Filters
IEEE Transactions on Software Engineering
The Palladio component model for model-driven performance prediction
Journal of Systems and Software
Reverse Engineering Software-Models of Component-Based Systems
CSMR '08 Proceedings of the 2008 12th European Conference on Software Maintenance and Reengineering
Predicting the performance of component-based software architectures with different usage profiles
QoSA'07 Proceedings of the Quality of software architectures 3rd international conference on Software architectures, components, and applications
Proceedings of the 2nd ACM/SPEC International Conference on Performance engineering
Self-aware QoS management in virtualized infrastructures
Proceedings of the 8th ACM international conference on Autonomic computing
An adaptive fine-grained performance modeling approach for internetware
Proceedings of the Second Asia-Pacific Symposium on Internetware
Automated simulation-based capacity planning for enterprise data fabrics
Proceedings of the 4th International ICST Conference on Simulation Tools and Techniques
Statistical inference of software performance models for parametric performance completions
QoSA'10 Proceedings of the 6th international conference on Quality of Software Architectures: research into Practice - Reality and Gaps
Automated extraction of architecture-level performance models of distributed component-based systems
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
Capacity planning for event-based systems using automated performance predictions
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
Proceedings of the 8th international ACM SIGSOFT conference on Quality of Software Architectures
Quality prediction in service composition frameworks
ICSOC'11 Proceedings of the 2011 international conference on Service-Oriented Computing
Proceedings of the 2013 ACM SIGPLAN international conference on Object oriented programming systems languages & applications
Constructing performance model of JMS middleware platform
Proceedings of the 5th ACM/SPEC international conference on Performance engineering
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Nowadays, software systems have to fulfill increasingly stringent requirements for performance and scalability. To ensure that a system meets its performance requirements during operation, the ability to predict its performance under different configurations and workloads is essential. Most performance analysis tools currently used in industry focus on monitoring the current system state. They provide low-level monitoring data without any performance prediction capabilities. For performance prediction, performance models are normally required. However, building predictive performance models manually requires a lot of time and effort. In this paper, we present a method for automated extraction of performance models of Java EE applications, based on monitoring data collected during operation. We extract instances of the Palladio Component Model (PCM) - a performance meta-model targeted at component-based systems. We evaluate the model extraction method in the context of a case study with a real-world enterprise application. Even though the extraction requires some manual intervention, the case study demonstrates that the existing gap between low-level monitoring data and high-level performance models can be closed.