Hostload prediction in a Google compute cloud with a Bayesian model
Venue
Supercomputing 2012
Publication Year
2012
Authors
Sheng Di, Derrick Kondo, Walfredo Cirne
BibTeX
Abstract
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.
