Improving rule-based elasticity control by adapting the sensitivity of the auto-scaling decision timeframe

Demetris Trihinas, Zacharias Georgiou, George Pallis, Marios D. Dikaiakos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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%.

Original languageEnglish
Title of host publicationAlgorithmic Aspects of Cloud Computing - 3rd International Workshop, ALGOCLOUD 2017, Revised Selected Papers
EditorsAlex Delis, George Pallis, Dan Alistarh
PublisherSpringer Verlag
Pages123-137
Number of pages15
ISBN (Print)9783319748740
DOIs
Publication statusPublished - 1 Jan 2018
Event3rd International Workshop on Algorithmic Aspects of Cloud Computing, ALGOCLOUD 2017 - Vienna, Austria
Duration: 5 Sep 20175 Sep 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10739 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Workshop on Algorithmic Aspects of Cloud Computing, ALGOCLOUD 2017
CountryAustria
CityVienna
Period5/09/175/09/17

Keywords

  • Auto-scaling
  • Cloud computing
  • Cloud monitoring
  • Elasticity

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  • Cite this

    Trihinas, D., Georgiou, Z., Pallis, G., & Dikaiakos, M. D. (2018). Improving rule-based elasticity control by adapting the sensitivity of the auto-scaling decision timeframe. In A. Delis, G. Pallis, & D. Alistarh (Eds.), Algorithmic Aspects of Cloud Computing - 3rd International Workshop, ALGOCLOUD 2017, Revised Selected Papers (pp. 123-137). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10739 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-74875-7_8