Effective Prediction of Job Processing Times in a Large-Scale Grid Environment

Grid applications that use a considerable number of processors for their computations need effective predictions of the expected computation times on the different nodes. Currently, there are no effective prediction methods available that satisfactorily cope with those ever-changing dynamics of computation times in a grid environment. Motivated by this, in this paper we develop the Dynamic Exponential Smoothing (DES) method to predict job processing times in a grid environment. To compare predictions of DES to those of the existing prediction methods, we have performed extensive experiments in a real large-scale grid environment. The results illustrate a strong and consistent improvement of DES in comparison with the existing prediction methods. 1