Grid computing is an emerging technology by which huge numbers of processors over the world create a global source of processing power. Their collaboration makes it possible to perform computations that are too extensive to perform on a single processor. On a grid processors may connect and disconnect at any time, and the load on the computers can be highly bursty. Those characteristics raise the need for the development of techniques that make grid applications robust against the dynamics of the grid environment. In particular, applications that use significant amounts of processor power for running jobs need effective predictions of the expected computation times of those jobs on remote hosts. Currently, there are no effective prediction methods available that cope with the ever-changing running times of jobs on a grid environment. Motivated by this, we develop the Dynamic Exponential Smoothing (DES) method to predict running times in a grid environment. The main idea behind DES is that it dynamically adapts its prediction strategy to the height of the fluctuations in those running times. We have performed extensive experiments in a real global-scale grid environment to compare the effectiveness of DES. The results demonstrate that DES strongly and consistently outperforms existing prediction methods.
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.
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