Large-scale systems like BlueGene/L are susceptible to a number of software and hardware failures that can affect system performance. In this paper evaluate the effectiveness of a previously developed job scheduling algorithm for BlueGene/L in the presence of faults. We have developed two new job-scheduling algorithms considering failures while scheduling the jobs. We have also evaluated the impact of these algorithms on average bounded slowdown, average response time and system utilization, considering different levels of proactive failure prediction and prevention techniques reported in the literature. Our simulation studies show that the use of these new algorithms with even trivial fault prediction confidence or accuracy levels (as low as 10%) can significantly improve the performance of the BlueGene/L system.