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.
%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
%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 = {2020-04-06T11:24:47.000+0200},
author = {Grohmann, Johannes and Eismann, Simon and Kounev, Samuel},
biburl = {https://www.bibsonomy.org/bibtex/28a1705f19ce2286c9360edf8ba99cab2/se-group},
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},
month = {April},
pages = {60--63},
timestamp = {2021-01-12T13:08:14.000+0100},
title = {{The Vision of Self-Aware Performance Models}},
url = {https://doi.org/10.1109/ICSA-C.2018.00024},
year = 2018
}