Computer Science Department
School of Computer Science, Carnegie Mellon University
An Evaluation of Linear Models for Host Load Prediction
Peter A. Dinda, David R. O'Hallaron
November 1998
CMU-CS-98-148.ps
CMU-CS-98-148.pdf
This paper evaluates linear models for predicting the Digital Unix
five-second load average from 1 to 30 seconds into the future. A
detailed statistical study of a large number of load traces leads to
consideration of the Box-Jenkins models (AR, MA, ARMA, ARIMA), and the
ARFIMA models (due to self-similarity.) These models, as well as a
simple windowed-mean scheme, are evaluated by running a large number
of randomized testcases on the load traces. The main conclusions are
that load is consistently predictable to a useful degree, and that the
simpler models such as AR are sufficient for doing this prediction.
29 pages
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