Publication details

G. Lanciano, F. Galli, T. Cucinotta, D. Bacciu, A. Passarella. "Extending OpenStack Monasca for Predictive Elasticity Control," Big-Data Mining and Analytics, Tsinghua University Press, Vol. 7, Issue 2, June 2024

Abstract

Traditional auto-scaling approaches are conceived as reactive automations, typically triggered when predefined thresholds are breached by resource consumption metrics. Managing such rules at scale is cumbersome, especially when resources require non-negligible times to be instantiated. This paper introduces an architecture for predictive cloud operations, that enables orchestrators to apply time-series forecasting techniques to estimate the evolution of relevant metrics, and take decisions based on the predicted state of the system. In this way, e.g., they can anticipate load peaks, and trigger appropriate scaling actions in advance, such that new resources are available when needed. The proposed architecture was implemented in OpenStack, extending the monitoring capabilities of Monasca by injecting short-term forecasts of standard metrics. We used our architecture to implement predictive scaling policies leveraging on linear regression, autoregressive integrated moving average, feed-forward and recurrent neural networks (RNN). Then, we evaluated their performance on a synthetic workload, comparing them to those of a traditional policy. To assess the ability of the different models to generalize to unseen patterns, we also evaluated them on traces from a real CDN workload. In particular, the RNN model exhibited the best overall performance in terms of prediction error, observed client-side response latency, and forecasting overhead. The implementation of our architecture is open-source.

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DOI: 10.26599/BDMA.2023.9020014

BibTeX entry:

@article{Lanciano_2024,
     title={Extending OpenStack Monasca for Predictive Elasticity Control},
     volume={7},
     ISSN={2097-406X},
     url={http://dx.doi.org/10.26599/BDMA.2023.9020014},
     DOI={10.26599/bdma.2023.9020014},
     number={2},
     journal={Big Data Mining and Analytics},
     publisher={Tsinghua University Press},
     author={Lanciano, Giacomo and Galli, Filippo and Cucinotta, Tommaso and Bacciu, Davide and Passarella, Andrea},
     year={2024},
     month=jun,
     pages={315–339}
}

Last updated on
07 November 2024