With the advent of the micro-service paradigm, applications are divided into small, distributed parts. Knowledge of optimal resource configurations of such applications is required both for autonomic resource management as well as its assessment. Due to the high-dimensional search space of all possible configurations, the systematic measuring of the optimal configurations is challenging. To this end, we introduce a search algorithm based on hill-climbing for finding all optimal configurations in a feasible time and integrate it in an existing measuring framework. This approach enables the assessment, comparison and optimization of autonomic resource management approaches for micro-service applications. The evaluation shows that our approach is able to find all optimal configurations in the considered scenarios, while state-of-the-art multi-objective search algorithms do not.
%0 Conference Paper
%1 BaEiGrHeKo-ICAC-BUNGEE
%A Bauer, André
%A Eismann, Simon
%A Grohmann, Johannes
%A Herbst, Nikolas
%A Kounev, Samuel
%B 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W)
%C Los Alamitos, CA, USA
%D 2019
%I IEEE Computer Society
%K BUNGEE Cloud Elasticity Multi-criteria_optimization Optimization Resource_management descartes t_workshop myown
%P 120--125
%R 10.1109/FAS-W.2019.00040
%T Systematic Search for Optimal Resource Configurations of Distributed Applications
%U https://doi.ieeecomputersociety.org/10.1109/FAS-W.2019.00040
%X With the advent of the micro-service paradigm, applications are divided into small, distributed parts. Knowledge of optimal resource configurations of such applications is required both for autonomic resource management as well as its assessment. Due to the high-dimensional search space of all possible configurations, the systematic measuring of the optimal configurations is challenging. To this end, we introduce a search algorithm based on hill-climbing for finding all optimal configurations in a feasible time and integrate it in an existing measuring framework. This approach enables the assessment, comparison and optimization of autonomic resource management approaches for micro-service applications. The evaluation shows that our approach is able to find all optimal configurations in the considered scenarios, while state-of-the-art multi-objective search algorithms do not.
@inproceedings{BaEiGrHeKo-ICAC-BUNGEE,
abstract = {With the advent of the micro-service paradigm, applications are divided into small, distributed parts. Knowledge of optimal resource configurations of such applications is required both for autonomic resource management as well as its assessment. Due to the high-dimensional search space of all possible configurations, the systematic measuring of the optimal configurations is challenging. To this end, we introduce a search algorithm based on hill-climbing for finding all optimal configurations in a feasible time and integrate it in an existing measuring framework. This approach enables the assessment, comparison and optimization of autonomic resource management approaches for micro-service applications. The evaluation shows that our approach is able to find all optimal configurations in the considered scenarios, while state-of-the-art multi-objective search algorithms do not. },
added-at = {2020-04-06T11:25:21.000+0200},
address = {Los Alamitos, CA, USA},
author = {Bauer, Andr{\'e} and Eismann, Simon and Grohmann, Johannes and Herbst, Nikolas and Kounev, Samuel},
biburl = {https://www.bibsonomy.org/bibtex/2bddb3f25d3c5a80fea296f50b60d80a0/joh.grohmann},
booktitle = {2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W)},
doi = {10.1109/FAS-W.2019.00040},
interhash = {43f3bd709d47f3d558007e3fe18df90f},
intrahash = {bddb3f25d3c5a80fea296f50b60d80a0},
keywords = {BUNGEE Cloud Elasticity Multi-criteria_optimization Optimization Resource_management descartes t_workshop myown},
month = jun,
pages = {120--125},
publisher = {IEEE Computer Society},
timestamp = {2022-11-16T09:10:36.000+0100},
title = {{Systematic Search for Optimal Resource Configurations of Distributed Applications}},
url = {https://doi.ieeecomputersociety.org/10.1109/FAS-W.2019.00040},
year = 2019
}