%0 Conference Paper %1 BrKoKr2009-ROSSA-Extracting_PCM_JavaEE %A Brosig, Fabian %A Kounev, Samuel %A Krogmann, Klaus %B Proceedings of the 1st International Workshop on Run-time mOdels for Self-managing Systems and Applications (ROSSA 2009). In conjunction with Fourth International Conference on Performance Evaluation Methodologies and Tools (VALUETOOLS 2009), Pisa, Italy, October 19, 2009. %D 2009 %I ACM, New York, NY, USA %K Analytical_and_simulation-based_analysis Application_quality_of_service_management Automated_model_learning Formal_architecture_modeling Instrumentation_profiling_and_workload_characterization Meta-models Performance Prediction Resource_management Statistical_estimation_and_machine_learning descartes t_workshop myown %T Automated Extraction of Palladio Component Models from Running Enterprise Java Applications %X 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.

@inproceedings{BrKoKr2009-ROSSA-Extracting_PCM_JavaEE, abstract = {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.}, added-at = {2020-04-05T23:07:45.000+0200}, author = {Brosig, Fabian and Kounev, Samuel and Krogmann, Klaus}, biburl = {https://www.bibsonomy.org/bibtex/29294d4ef96c8778cd766bfeb82c74a93/samuel.kounev}, booktitle = {Proceedings of the 1st International Workshop on Run-time mOdels for Self-managing Systems and Applications (ROSSA 2009). In conjunction with Fourth International Conference on Performance Evaluation Methodologies and Tools (VALUETOOLS 2009), Pisa, Italy, October 19, 2009.}, interhash = {a259f0e52184ae2c61180d473efb59cb}, intrahash = {9294d4ef96c8778cd766bfeb82c74a93}, keywords = {Analytical_and_simulation-based_analysis Application_quality_of_service_management Automated_model_learning Formal_architecture_modeling Instrumentation_profiling_and_workload_characterization Meta-models Performance Prediction Resource_management Statistical_estimation_and_machine_learning descartes t_workshop myown}, month = {October}, publisher = {ACM, New York, NY, USA}, timestamp = {2020-10-21T04:00:47.000+0200}, title = {{Automated Extraction of Palladio Component Models from Running Enterprise Java Applications}}, year = 2009 }