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An evaluation of linear models for host load prediction

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2 Author(s)
P. A. Dinda ; Carnegie Mellon Univ., Pittsburgh, PA, USA ; D. R. O'Hallaron

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 (1994) models (AR, MA, ARMA, ARIMA), and the ARFIMA (autoregressive fractional integrated moving average) 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 test cases on the load traces and by data-mining their results. The main conclusions are that the load is consistently predictable to a very useful degree, and that the simpler models, such as AR, are sufficient for performing this prediction

Published in:

High Performance Distributed Computing, 1999. Proceedings. The Eighth International Symposium on

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