Automated tiering in a QoS environment using sparse data

We present a method for providing automated performance management for a storage array with quality of service demands. Our method dynamically configures application data to heterogeneous storage devices, taking into account response time targets for the different applications. The optimization process uses widely available coarse counter statistics. We use analytical modeling of the storage system components and a gradient descent mechanism to minimize the target function. We verify the effectiveness of our optimization procedure using traces from real production systems, and show that our method improves the performance of the more significant applications, and can do so using only the coarse statistics. Our results show substantial improvements for preferred applications over an optimization procedure which does not take QoS requirements into account. We also present evidence of the complex behavior of real production storage systems, which has to be taken into account in the optimization process.