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 language||English (US)|
|Number of pages||10|
|Journal||IEEE International Symposium on High Performance Distributed Computing, Proceedings|
|State||Published - Dec 1 1999|
ASJC Scopus subject areas
- Hardware and Architecture