Improving Rule-Based Elasticity Control by Adapting the Sensitivity of the Auto-Scaling Decision Timeframe [chapter]

Demetris Trihinas, Zacharias Georgiou, George Pallis, Marios D. Dikaiakos
2018 Lecture Notes in Computer Science  
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
more » ... g actions and thus, significant costs. In this paper, we introduce AdaFrame, a novel library which supports the decision-making of rulebased 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%.
doi:10.1007/978-3-319-74875-7_8 fatcat:ukrv35p7srevjelwsvbxqwax3e