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

Sheng Di 1, * Derrick Kondo 1, 2 Walfredo Cirne 3
* Corresponding author
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.
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https://hal.inria.fr/hal-00788002
Contributor : Arnaud Legrand <>
Submitted on : Wednesday, February 13, 2013 - 2:57:29 PM
Last modification on : Thursday, October 11, 2018 - 8:48:02 AM

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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. ⟨hal-00788002⟩

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