Using Historical Data to Predict Application Runtimes on Backfilling Parallel Systems

We present in this paper a novel method to predict application runtimes on backfilling parallel systems. The method is based on mining historical data to obtain important parameters. These parameters are then applied to predict the runtime of future applications. It has been shown in previous works that both underestimate and inaccuracy in prediction have adverse impacts on scheduling performance of backfilling systems. In our study, we try to reduce the number of jobs that are underestimated and reduce the prediction error as much as possible. Comparing with other predictors, experimental results show that our predictor is up to 25% better with respect to the problem of underestimate. Moreover, using the metric proposed in for the accuracy, our predictor improves up to 32%.