Enhancing Prediction on Non-dedicated Clusters

In this paper, we present a scheduling scheme to estimate the turnaround time of parallel jobs on a heterogeneous and non-dedicated cluster or NoW(Network of Workstations). This scheme is based on an analytical pre- diction model that establishes the processing and communication slowdown of the execution times of the jobs based on the cluster nodes and links powerful and occupancy. Preservation of the local application responsiveness is also a goal. We address the impact of inaccuracies in these estimates on the overall system performance. Furthermore, we demonstrate that job scheduling benefits from the accuracy of these estimates. The app licability of our proposal has been proved by measuring the efficiency of our method by comparing the predicted deviations of the parallel jobs in a real environment with respect to the most representative ones of the literature. The additional cost of obtaining these was also evaluated and compared. The present work is implemented within the CISNE project, a previously devel- oped scheduling framework for non-dedicated and heterogeneous cluster environments