Host load prediction using linear models

Peter A. Dinda, David R. O'Hallaron

Research output: Contribution to journalArticlepeer-review

Abstract

This paper evaluates linear models for predicting the Digital Unix fivedashsecond 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 selfdashsimilarity.) We also consider a simple windoweddashmean model. The computational requirements of these models span a wide range, making some more practical than others for incorporation into an online prediction system. We rigorously evaluate the predictive power of the models by running a large number of randomized testcases on the load traces and then datadashmining their results. The main conclusions are that load is consistently predictable to a very useful degree, and that the simple, practical models such as AR are sufficient for host load prediction. We recommend AR(16) models or better for host load prediction. We implement an online host load prediction system around the AR(16) model and evaluate its overhead, finding that it uses miniscule amounts of CPU time and network bandwidth.
Original languageEnglish
Pages (from-to)265-280
JournalCluster Computing
Volume3
DOIs
StatePublished - 2000

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