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

Keywords: Host load prediction, linear time series models, statistical analysis of host load, self-similarity, epochal behavior

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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