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

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10739)


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%.


Cloud computing Auto-scaling Elasticity Cloud monitoring 



This work is partially supported by the European Commission in terms of Unicorn 731846 H2020 project (H2020-ICT-2016-1).


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of CyprusNicosiaCyprus

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