New Metrics for Disk Failure Prediction That Go Beyond Prediction Accuracy

Abstract

Prediction accuracy (true positives, false positives, and so on) is the usual way for evaluating disk-failure prediction models. Realistically however, we aim not only to correctly predict failures, but also to protect data against failure, i.e., we need to take appropriate action after a failure prediction. In the context of storage systems, protecting data requires that we migrate at-risk data, but this consumes network and disk bandwidth, which is particularly problematic for large-scale and cloud systems. This paper consolidates and builds on Li et al. (2016), where we propose using two new metrics, migration rate (MR) and mismigration rate (MMR), to measure the quality of disk failure prediction: MR measures how much at-risk data is migrated (and therefore protected) as a result of correct failure predictions, while MMR measures how much data is migrated needlessly as a result of incorrect failure predictions. In this paper, we additionally propose measuring quality in terms of migration time and mismigration time, which measure the time spent migrating at-risk disks, and the time spent mismigrating healthy disks caused by false alarms, respectively. To demonstrate these metrics’ usefulness, we use them to compare disk-failure prediction methods: we compare: 1) a classification tree (CT) model against a state-of-the-art recurrent neural network (RNN) model and 2) a gradient-boosted regression tree (GBRT) model (which predicts residual life) against RNN. We observe that while RNN performs best in the prediction accuracy experiments, the CT and GBRT models sometimes outperform RNN in the resource-dependent migration-rate experiments. We conclude that prediction accuracy is sometimes misleading: correct predictions do not necessarily imply protected data. We additionally present an improved GBRT model (GBRT+), which offers a practical improvement in disk residual-life prediction accordingly to the newly proposed metrics.