An Expandable Extraction Framework for Architectural Performance Models
J. Walter, C. Stier, H. Koziolek, and S. Kounev. Proceedings of the 3rd International Workshop on Quality-Aware DevOps (QUDOS'17), ACM, (April 2017)
Abstract
Providing users with Quality of Service (QoS) guarantees and the prevention of performance problems are challenging tasks for software systems. Architectural performance models can be applied to explore performance properties of a software system at design time and run time. At design time, architectural performance models support reasoning on effects of design decisions. At run time, they enable automatic reconfigurations by reasoning on the effects of changing user behavior. In this paper, we present a framework for the extraction of architectural performance models based on monitoring log files generalizing over the targeted architectural modeling language. Using the presented framework, the creation of a performance model extraction tool for a specific modeling formalism requires only the implementation of a key set of object creation routines specific to the formalism. Our framework integrates them with extraction techniques that apply to many architectural performance models, e.g., resource demand estimation techniques. This lowers the effort to implement performance model extraction tools tremendously through a high level of reuse. We evaluate our framework presenting builders for the Descartes Modeling Language (DML) and the Palladio Component Model (PCM). For the extracted models we compare simulation results with measurements receiving accurate results.
%0 Conference Paper
%1 WaStKoKo2017-QUDOS-PMXBuilder
%A Walter, Jürgen
%A Stier, Christian
%A Koziolek, Heiko
%A Kounev, Samuel
%B Proceedings of the 3rd International Workshop on Quality-Aware DevOps (QUDOS'17)
%D 2017
%I ACM
%K Application_quality_of_service_management Automated_model_learning DML Meta-models PMX Performance Statistical_estimation_and_machine_learning Tool descartes t_workshop
%T An Expandable Extraction Framework for Architectural Performance Models
%X Providing users with Quality of Service (QoS) guarantees and the prevention of performance problems are challenging tasks for software systems. Architectural performance models can be applied to explore performance properties of a software system at design time and run time. At design time, architectural performance models support reasoning on effects of design decisions. At run time, they enable automatic reconfigurations by reasoning on the effects of changing user behavior. In this paper, we present a framework for the extraction of architectural performance models based on monitoring log files generalizing over the targeted architectural modeling language. Using the presented framework, the creation of a performance model extraction tool for a specific modeling formalism requires only the implementation of a key set of object creation routines specific to the formalism. Our framework integrates them with extraction techniques that apply to many architectural performance models, e.g., resource demand estimation techniques. This lowers the effort to implement performance model extraction tools tremendously through a high level of reuse. We evaluate our framework presenting builders for the Descartes Modeling Language (DML) and the Palladio Component Model (PCM). For the extracted models we compare simulation results with measurements receiving accurate results.
@inproceedings{WaStKoKo2017-QUDOS-PMXBuilder,
abstract = {Providing users with Quality of Service (QoS) guarantees and the prevention of performance problems are challenging tasks for software systems. Architectural performance models can be applied to explore performance properties of a software system at design time and run time. At design time, architectural performance models support reasoning on effects of design decisions. At run time, they enable automatic reconfigurations by reasoning on the effects of changing user behavior. In this paper, we present a framework for the extraction of architectural performance models based on monitoring log files generalizing over the targeted architectural modeling language. Using the presented framework, the creation of a performance model extraction tool for a specific modeling formalism requires only the implementation of a key set of object creation routines specific to the formalism. Our framework integrates them with extraction techniques that apply to many architectural performance models, e.g., resource demand estimation techniques. This lowers the effort to implement performance model extraction tools tremendously through a high level of reuse. We evaluate our framework presenting builders for the Descartes Modeling Language (DML) and the Palladio Component Model (PCM). For the extracted models we compare simulation results with measurements receiving accurate results.},
added-at = {2020-04-05T23:16:24.000+0200},
author = {Walter, J{\"u}rgen and Stier, Christian and Koziolek, Heiko and Kounev, Samuel},
biburl = {https://www.bibsonomy.org/bibtex/23b620e50ed652a7b7850158dd6a18b57/se-group},
booktitle = {{Proceedings of the 3rd International Workshop on Quality-Aware DevOps (QUDOS'17)}},
interhash = {db39dfff6468dc9db28a8c55e443bd9a},
intrahash = {3b620e50ed652a7b7850158dd6a18b57},
keywords = {Application_quality_of_service_management Automated_model_learning DML Meta-models PMX Performance Statistical_estimation_and_machine_learning Tool descartes t_workshop},
month = {April},
publisher = {ACM},
timestamp = {2020-10-06T14:18:12.000+0200},
title = {{An Expandable Extraction Framework for Architectural Performance Models}},
year = 2017
}