Dynamic forecasting of the completion time of a computational experiment in a Desktop Grid

Desktop Grid is a powerful tool to perform high-throughput computing. Desktop Grid is a form of distributed high-throughput computing system, which uses idle time of non-dedicated geographically distributed computing nodes connected over low-speed network. It has significant differences from computing clusters and computational GRIDs, and needs special operation tackle.  In this paper, we present a mechanism for dynamic forecasting of the completion time of a computational experiment in a Desktop Grid. We propose a statistical approach based on the linear regression model with the calculation of a confidence interval, taking into account the accumulation of statistical error and, if needed, changing the forecast. The developed approach is used to implement a forecasting algorithm and software module for a Desktop Grid. We present experimental results based on data from the RakeSearch volunteer computing project.