Homeostatic and Tendency-Based CPU Load Predictions

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.