Disk array performance prediction with CART-MARS hybrid models

This work presents a new black-box learning-based model to predict the performance of disk array. We focus on predicting how the change of performance with comprised different disks in a disk array. We use CART-MARS hybrid models: the CART methods to model the correlation between the workload and disk array. The MARS methods to construct the delta model for pairs of disk arrays. Compared with direct prediction of performance, the delta model avoids the complexity of modeling the internal architecture and algorithms of a disk array system. Our experiments show that the error ratio of the prediction of delta model is reduced to 16%. By comparison, the error ratio of the direct prediction is 29%, which is almost two times larger than delta model.