Evaluation of linear models for host load prediction

Peter A. Dinda*, David R. O'Hallaron

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

72 Scopus citations


This paper evaluates linear models for predicting the Digital Unixfive-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.

Original languageEnglish (US)
Pages (from-to)87-96
Number of pages10
JournalIEEE International Symposium on High Performance Distributed Computing, Proceedings
StatePublished - Dec 1 1999

ASJC Scopus subject areas

  • Hardware and Architecture

Fingerprint Dive into the research topics of 'Evaluation of linear models for host load prediction'. Together they form a unique fingerprint.

Cite this