%0 Conference Paper %1 GrEiKo-ICSA18-Vision %A Grohmann, Johannes %A Eismann, Simon %A Kounev, Samuel %B 2018 IEEE International Conference on Software Architecture Companion (ICSA-C) %D 2018 %K Analytical_and_simulation-based_analysis Automated_model_learning DML Online_monitoring_and_forecasting Optimization Performance Prediction Self-adaptive-systems Self-aware-computing Simulation Statistical_estimation_and_machine_learning descartes t_visionposition myown %P 60--63 %R 10.1109/ICSA-C.2018.00024 %T The Vision of Self-Aware Performance Models %U https://doi.org/10.1109/ICSA-C.2018.00024 %X Performance models are necessary components of self-aware computing systems, as they allow such systems to reason about their own state and behavior. Research in this field has developed a multitude of approaches to create, maintain, and solve performance models. In this paper, we propose a meta-self-aware computing approach making the processes of model creation, maintenance and solution themselves self-aware. This enables the automated selection and adaption of software performance engineering approaches specifically tailored to the system under study.

@inproceedings{GrEiKo-ICSA18-Vision, abstract = {Performance models are necessary components of self-aware computing systems, as they allow such systems to reason about their own state and behavior. Research in this field has developed a multitude of approaches to create, maintain, and solve performance models. In this paper, we propose a meta-self-aware computing approach making the processes of model creation, maintenance and solution themselves self-aware. This enables the automated selection and adaption of software performance engineering approaches specifically tailored to the system under study. }, added-at = {2020-04-06T11:24:46.000+0200}, author = {Grohmann, Johannes and Eismann, Simon and Kounev, Samuel}, biburl = {https://www.bibsonomy.org/bibtex/28a1705f19ce2286c9360edf8ba99cab2/samuel.kounev}, booktitle = {2018 IEEE International Conference on Software Architecture Companion (ICSA-C)}, doi = {10.1109/ICSA-C.2018.00024}, interhash = {0b25b9f7a73153b3c77e126b6e841d4f}, intrahash = {8a1705f19ce2286c9360edf8ba99cab2}, keywords = {Analytical_and_simulation-based_analysis Automated_model_learning DML Online_monitoring_and_forecasting Optimization Performance Prediction Self-adaptive-systems Self-aware-computing Simulation Statistical_estimation_and_machine_learning descartes t_visionposition myown}, month = {April}, pages = {60--63}, timestamp = {2022-11-16T09:10:05.000+0100}, title = {{The Vision of Self-Aware Performance Models}}, url = {https://doi.org/10.1109/ICSA-C.2018.00024}, year = 2018 }