Failure prediction in IBM BlueGene/L event logs

In this paper, we present our effort in developing a failure prediction model based on event logs collected from IBM BlueGene/L. We first show how the event records can be converted into a data set that is appropriate for running classification techniques. Then we apply classifiers on the data, including RIPPER (a rule-based classifier), support vector machines (SVMs), a traditional Nearest Neighbor method, and a customized nearest neighbor method. We show that the customized nearest neighbor approach can outperform RIPPER and SVMs in terms of both coverage and precision. The results suggest that the customized nearest neighbor approach can be used to alleviate the impact of failures.