Argonne National Laboratory

Homeostatic and Tendency-based CPU Load Predictions

TitleHomeostatic and Tendency-based CPU Load Predictions
Publication TypeReport
Year of Publication2002
AuthorsYang, L, Foster, IT, Schopf, JM
Date Published09/2002
Other NumbersANL/MCS-P997-0902

The dynamic nature of a resource-sharing environment means that applications must be able to adapt their behavior in response to changes in system status. Predictions of future system performance can be used to guide such adaptations. In this paper, we present and evaluate several new one-step-ahead and low-overhead time series prediction strategies that track recent trends by giving more weight to recent data. We present results that show that a dynamic tendency prediction model with different ascending and descending behavior performs best among all strategies studied. A comparative study conducted on a set of 38 machine load traces shows that this new predictor achieves average prediction errors that are between 2% and 55% less (36% less on average) than those incurred by the predictors used within the popular Network Weather Service system.