TY - JOUR
T1 - Evaluation of linear models for host load prediction
AU - Dinda, Peter A.
AU - O'Hallaron, David R.
PY - 1999/12/1
Y1 - 1999/12/1
N2 - 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.
AB - 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.
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M3 - Article
AN - SCOPUS:0033364817
SP - 87
EP - 96
JO - Proceedings of the IEEE International Symposium on High Performance Distributed Computing
JF - Proceedings of the IEEE International Symposium on High Performance Distributed Computing
SN - 1082-8907
ER -