Host Load Prediction in a Google Compute Cloud with a Bayesian Model

Sheng Di 1, * Derrick Kondo 1, 2 Walfredo Cirne 3
* Auteur correspondant
1 MESCAL - Middleware efficiently scalable
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
3 Google Inc.
Google Inc [Mountain View]
Abstract : Prediction of host load in Cloud systems is critical for achieving service-level agreements. However, accurate prediction of host load in Clouds is extremely challenging because it fluctuates drastically at small timescales. We design a prediction method based on Bayes model to predict the mean load over a long-term time interval, as well as the mean load in consecutive future time intervals. We identify novel predictive features of host load that capture the expectation, predictability, trends and patterns of host load. We also determine the most effective combinations of these features for prediction. We evaluate our method using a detailed one-month trace of a Google data center with thousands of machines. Experiments show that the Bayes method achieves high accuracy with a mean squared error of 0.0014. Moreover, the Bayes method improves the load prediction accuracy by 5.6-50% compared to other state-of-the-art methods based on moving averages, auto-regression, and/or noise filters.
Type de document :
Communication dans un congrès
IEEE/ACM Supercomputing Conference (SC), 2012, Unknown, 2012
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https://hal.inria.fr/hal-00788002
Contributeur : Arnaud Legrand <>
Soumis le : mercredi 13 février 2013 - 14:57:29
Dernière modification le : mardi 16 janvier 2018 - 15:35:29

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  • HAL Id : hal-00788002, version 1

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Sheng Di, Derrick Kondo, Walfredo Cirne. Host Load Prediction in a Google Compute Cloud with a Bayesian Model. IEEE/ACM Supercomputing Conference (SC), 2012, Unknown, 2012. 〈hal-00788002〉

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