Hostload prediction in a Google compute cloud with a Bayesian model

Prediction of host load in Cloud systems is crit-
ical 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, predictabil-
ity, 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.

• Software Systems

• Distributed Systems and Parallel Computing