An Evaluation of Linear Models for Host Load Prediction

Peter A. Dinda and David R. O'Hallaron
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213

Abstract: This paper evaluates linear models for predicting the
Digital Unix five-second host load average from 1 to 30 seconds into
the future.  A detailed statistical study of a large number of long,
fine grain load traces from a variety of real machines 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 then rigorously evaluated by running
a large number of randomized testcases on the load traces and
data-mining their results. The main conclusions are that load is
consistently predictable to a very useful degree, and that the simpler
models such as AR are sufficient for doing this prediction.

@inproceedings	(dindahpdc99,
author	=	"P. Dinda and D. O'Hallaron",
title	=	"An Evaluation of Linear Models for Host Load Prediction", 
booktitle=	"Proc. 8th IEEE Symposium on High-Performance
		  Distributed Computing (HPDC-8)" ,
month	=	aug ,
address	=	"Redondo Beach, CA" ,
year	=	"1999" ,
)