@inproceedings{95c81c62f2cb431ea65550f660762546,
title = "Improving rule-based elasticity control by adapting the sensitivity of the auto-scaling decision timeframe",
abstract = "Cloud computing offers the opportunity to improve efficiency with cloud providers offering consumers the ability to automatically scale their applications to meet exact demands. However, “auto-scaling” is usually provided to consumers in the form of metric threshold rules which are not capable of determining whether a scaling alert is issued due to an actual change in the demand of the application or due to short-lived bursts evident in monitoring data. The latter, can lead to unjustified scaling actions and thus, significant costs. In this paper, we introduce AdaFrame, a novel library which supports the decision-making of rule-based elasticity controllers to timely detect actual runtime changes in the monitorable load of cloud services. Results on real-life testbeds deployed on AWS, show that AdaFrame is able to correctly identify scaling actions and in contrast to the AWS auto-scaler, is able to lower detection delay by at least 63%.",
keywords = "Auto-scaling, Cloud computing, Cloud monitoring, Elasticity",
author = "Demetris Trihinas and Zacharias Georgiou and George Pallis and Dikaiakos, {Marios D.}",
year = "2018",
month = jan,
day = "1",
doi = "10.1007/978-3-319-74875-7_8",
language = "English",
isbn = "9783319748740",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "123--137",
editor = "Alex Delis and George Pallis and Dan Alistarh",
booktitle = "Algorithmic Aspects of Cloud Computing - 3rd International Workshop, ALGOCLOUD 2017, Revised Selected Papers",
note = "3rd International Workshop on Algorithmic Aspects of Cloud Computing, ALGOCLOUD 2017 ; Conference date: 05-09-2017 Through 05-09-2017",
}