Improving Reliability with Dynamic Syndrome Allocation in Intelligent Software Defined Data Centers

We propose new algorithms for implementing a software-defined data center (SDDC) to improve the dependability of storage systems without the addition of new hardware. We define the construction of a system that can predict its future resource requirements and act on these predictions to allocate overprovisioned resources to improve reliability. We introduce algorithms for implementing a smart SDDC (S2DDC) that characterizes user I/O transactions (writes and deletes), and use these models to predict the level of overprovisioning within a system, overbooking excess resources to improve reliability, while mitigating the impact on quality of service. We compare several implementations of our methods experimentally, and discuss methods for improving the fault-tolerance of our S2DDC, present experimental results showcasing our ability to improve system reliability showing the decrease in expected annual block loss due to disk failures and latent sector errors, and highlight the benefit of dependence based usage models in estimating overprovisioning.