%0 Conference Paper %1 BrHuKo2011-ASE-AutomExtraction %A Brosig, Fabian %A Huber, Nikolaus %A Kounev, Samuel %B 26th IEEE/ACM International Conference On Automated Software Engineering (ASE 2011) %C Oread, Lawrence, Kansas %D 2011 %K Instrumentation_profiling_and_workload_characterization Resource_management Statistical_estimation_and_machine_learning t_full Performance Application_quality_of_service_management Prediction Automated_model_learning Formal_architecture_modeling Meta-models Analytical_and_simulation-based_analysis descartes %T Automated Extraction of Architecture-Level Performance Models of Distributed Component-Based Systems %X Modern service-oriented enterprise systems have increasingly complex and dynamic loosely-coupled architectures that often exhibit poor performance and resource efficiency and have high operating costs. This is due to the inability to predict at run-time the effect of dynamic changes in the system environment and adapt the system configuration accordingly. Architecture-level performance models provide a powerful tool for performance prediction, however, current approaches to modeling the execution context of software components are not suitable for use at run-time. In this paper, we analyze the typical online performance prediction scenarios and propose a novel performance meta-model for expressing and resolving parameter and context dependencies, specifically designed for use in online scenarios. We motivate and validate our approach in the context of a realistic and representative online performance prediction scenario based on the SPECjEnterprise2010 standard benchmark.

@inproceedings{BrHuKo2011-ASE-AutomExtraction, abstract = {Modern service-oriented enterprise systems have increasingly complex and dynamic loosely-coupled architectures that often exhibit poor performance and resource efficiency and have high operating costs. This is due to the inability to predict at run-time the effect of dynamic changes in the system environment and adapt the system configuration accordingly. Architecture-level performance models provide a powerful tool for performance prediction, however, current approaches to modeling the execution context of software components are not suitable for use at run-time. In this paper, we analyze the typical online performance prediction scenarios and propose a novel performance meta-model for expressing and resolving parameter and context dependencies, specifically designed for use in online scenarios. We motivate and validate our approach in the context of a realistic and representative online performance prediction scenario based on the SPECjEnterprise2010 standard benchmark.}, added-at = {2020-04-05T23:13:11.000+0200}, address = {Oread, Lawrence, Kansas}, author = {Brosig, Fabian and Huber, Nikolaus and Kounev, Samuel}, biburl = {https://www.bibsonomy.org/bibtex/208e4fb947240a4ddf9675a653a494a7f/se-group}, booktitle = {26th IEEE/ACM International Conference On Automated Software Engineering (ASE 2011)}, interhash = {c3cce1c97f1fde5c4041a6fe9d2db2a7}, intrahash = {08e4fb947240a4ddf9675a653a494a7f}, keywords = {Instrumentation_profiling_and_workload_characterization Resource_management Statistical_estimation_and_machine_learning t_full Performance Application_quality_of_service_management Prediction Automated_model_learning Formal_architecture_modeling Meta-models Analytical_and_simulation-based_analysis descartes}, month = {November}, note = {Acceptance Rate (Full Paper): 14.7\% (37/252)}, timestamp = {2020-10-05T16:27:21.000+0200}, title = {{A}utomated {E}xtraction of {A}rchitecture-{L}evel {P}erformance {M}odels of {D}istributed {C}omponent-{B}ased {S}ystems}, year = 2011 }