An Evaluation of Linear Models for Host Load Prediction

Peter A. Dinda and David R. O'Hallaron

Abstract: 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.  

@TechReport{dindaloadtr98,
  author = 	 "P. Dinda and D. O'Hallaron",
  title = 	 "An Evaluation of Linear Models for Host Load Prediction",
  institution =  "School of Computer Science, Carnegie Mellon University",
  year = 	 "1998",
  number = 	 "CMU-CS-TR-98-148",
  month = 	 nov,
}