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

@inproceedings{Trihinas2017ImprovingRE,
  title={Improving Rule-Based Elasticity Control by Adapting the Sensitivity of the Auto-Scaling Decision Timeframe},
  author={Demetris Trihinas and Zacharias Georgiou and George Pallis and Marios D. Dikaiakos},
  booktitle={ALGOCLOUD},
  year={2017}
}
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… 

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