%0 Conference Paper %1 BrHuKo2012-CBSE-ParamAndContextDep %A Brosig, Fabian %A Huber, Nikolaus %A Kounev, Samuel %B Proceedings of the 15th ACM SIGSOFT International Symposium on Component Based Software Engineering (CBSE 2012), June 26--28, 2012, Bertinoro, Italy %D 2012 %K Analytical_and_simulation-based_analysis Performance descartes Meta-models Application_quality_of_service_management Formal_architecture_modeling t_full myown Self-aware-computing Prediction DML %T Modeling Parameter and Context Dependencies in Online Architecture-Level Performance Models %U http://cbse-conferences.org/2012/ %X Modern enterprise applications have to satisfy increasingly stringent Quality-of-Service requirements. To ensure that a system meets its performance requirements, the ability to predict its performance under different configurations and workloads is essential. Architecture-level performance models describe performance-relevant aspects of software architectures and execution environments allowing to evaluate different usage profiles as well as system deployment and configuration options. However, building performance models manually requires a lot of time and effort. In this paper, we present a novel automated method for the extraction of architecture-level performance models of distributed component-based systems, based on monitoring data collected at run-time. The method is validated in a case study with the industry-standard SPECjEnterprise2010 Enterprise Java benchmark, a representative software system executed in a realistic environment. The obtained performance predictions match the measurements on the real system within an error margin of mostly 10-20 percent.

@inproceedings{BrHuKo2012-CBSE-ParamAndContextDep, abstract = {Modern enterprise applications have to satisfy increasingly stringent Quality-of-Service requirements. To ensure that a system meets its performance requirements, the ability to predict its performance under different configurations and workloads is essential. Architecture-level performance models describe performance-relevant aspects of software architectures and execution environments allowing to evaluate different usage profiles as well as system deployment and configuration options. However, building performance models manually requires a lot of time and effort. In this paper, we present a novel automated method for the extraction of architecture-level performance models of distributed component-based systems, based on monitoring data collected at run-time. The method is validated in a case study with the industry-standard SPECjEnterprise2010 Enterprise Java benchmark, a representative software system executed in a realistic environment. The obtained performance predictions match the measurements on the real system within an error margin of mostly 10-20 percent.}, added-at = {2020-04-05T23:07:42.000+0200}, author = {Brosig, Fabian and Huber, Nikolaus and Kounev, Samuel}, biburl = {https://www.bibsonomy.org/bibtex/292f259888c419f474f1c41c5511bf5c0/samuel.kounev}, booktitle = {Proceedings of the 15th ACM SIGSOFT International Symposium on Component Based Software Engineering (CBSE 2012), June 26--28, 2012, Bertinoro, Italy}, interhash = {31dcc838d5d60d523fa99daceca8aab1}, intrahash = {92f259888c419f474f1c41c5511bf5c0}, keywords = {Analytical_and_simulation-based_analysis Performance descartes Meta-models Application_quality_of_service_management Formal_architecture_modeling t_full myown Self-aware-computing Prediction DML}, month = {June}, note = {Acceptance Rate (Full Paper): 28.5\%.}, timestamp = {2020-10-05T16:31:25.000+0200}, title = {{Modeling Parameter and Context Dependencies in Online Architecture-Level Performance Models}}, url = {http://cbse-conferences.org/2012/}, year = 2012 }