Cloud Performance Variability Prediction
Yuxuan Zhao, Dmitry Duplyakin, Robert Ricci, Alexandru Uta · 2021
Cloud computing plays an essential role in our society nowadays. Many important services are highly dependant on the stable performance of the cloud. However, as prior work has shown, clouds exhibit large degrees of performance variability. Next to the stochastic variation induced by noisy neighbors, an important facet of cloud performance variability is given by changepoints---the instances where the non-stationary performance metrics exhibit persisting changes, which often last until subsequent changepoints occur. Such undesirable artifacts of the unstable application performance lead to problems with application performance evaluation and prediction efforts. Thus, characterization and understanding of performance changepoints become important elements of studying application performance in the cloud. In this paper, we showcase and tune two different changepoint detection methods, as well as demonstrate how the timing of the changepoints they identify can be predicted. We present a gradient-boosting-based prediction method, show that it can achieve good prediction accuracy, and give advice to practitioners on how to use our results.