State-based predictions with self-correction on Enterprise Desktop Grid environments
New prediction models based on heterogeneity and load-conditions are proposed. We evaluate the accuracy and error correlation in different load-conditions. We propose a prediction algorithm with error-correction adaptable to load-changes Our proposals with the error correction better capture behavior of the EDGC systems. Combining the proposed techniques with correction method provides the best accuracy.
enterprise desktop grids system-generated predictions instance-based learning application modeling dynamic prediction correction