%0 Conference Paper %1 straesser2023guideline %A Straesser, Martin %A Eismann, Simon %A von Kistowski, Jóakim %A Bauer, André %A Kounev, Samuel %B Proceedings of the 2023 ACM/SPEC International Conference on Performance Engineering %C New York, NY, USA %D 2023 %I Association for Computing Machinery %K antipatterns autoscaling descartes evaluation guideline methodology t_full %P 31-41 %R 10.1145/3578244.3583721 %T Autoscaler Evaluation and Configuration: A Practitioner's Guideline %U https://doi.org/10.1145/3578244.3583721 %X Autoscalers are indispensable parts of modern cloud deployments and determine the service quality and cost of a cloud application in dynamic workloads. The configuration of an autoscaler strongly influences its performance and is also one of the biggest challenges and showstoppers for the practical applicability of many research autoscalers. Many proposed cloud experiment methodologies can only be partially applied in practice, and many autoscaling papers use custom evaluation methods and metrics. This paper presents a practical guideline for obtaining meaningful and interpretable results on autoscaler performance with reasonable overhead. We provide step-by-step instructions for defining realistic usage behaviors and traffic patterns. We divide the analysis of autoscaler performance into a qualitative antipattern-based analysis and a quantitative analysis. To demonstrate the applicability of our guideline, we conduct several experiments with a microservice of our industry partner in a realistic test environment. %@ 9798400700682

@inproceedings{straesser2023guideline, abstract = {Autoscalers are indispensable parts of modern cloud deployments and determine the service quality and cost of a cloud application in dynamic workloads. The configuration of an autoscaler strongly influences its performance and is also one of the biggest challenges and showstoppers for the practical applicability of many research autoscalers. Many proposed cloud experiment methodologies can only be partially applied in practice, and many autoscaling papers use custom evaluation methods and metrics. This paper presents a practical guideline for obtaining meaningful and interpretable results on autoscaler performance with reasonable overhead. We provide step-by-step instructions for defining realistic usage behaviors and traffic patterns. We divide the analysis of autoscaler performance into a qualitative antipattern-based analysis and a quantitative analysis. To demonstrate the applicability of our guideline, we conduct several experiments with a microservice of our industry partner in a realistic test environment.}, added-at = {2023-02-15T00:05:19.000+0100}, address = {New York, NY, USA}, author = {Straesser, Martin and Eismann, Simon and von Kistowski, J\'{o}akim and Bauer, Andr\'{e} and Kounev, Samuel}, biburl = {https://www.bibsonomy.org/bibtex/25f67ce67489c44a323fad4c369dd2ed0/se-group}, booktitle = {Proceedings of the 2023 ACM/SPEC International Conference on Performance Engineering}, doi = {10.1145/3578244.3583721}, interhash = {0a0e169961a2f5f6d9fe87b045deb547}, intrahash = {5f67ce67489c44a323fad4c369dd2ed0}, isbn = {9798400700682}, keywords = {antipatterns autoscaling descartes evaluation guideline methodology t_full}, pages = {31-41}, publisher = {Association for Computing Machinery}, series = {ICPE '23}, timestamp = {2023-08-17T10:06:52.000+0200}, title = {Autoscaler Evaluation and Configuration: A Practitioner's Guideline}, url = {https://doi.org/10.1145/3578244.3583721}, venue = {Coimbra, Portugal}, year = 2023 }