Synthetic Data Generation for Storage Trace Augmentation
Lu Pang, Krishna Kant · ACM Transactions on Storage · 2025
Due to the increasingly data-intensive nature of the applications, the storage system performance continues to increase in importance and is often substantially responsible for the overall processing rate of the application. Fortunately, the storage technologies themselves are improving rapidly in numerous ways, from low-level read/write of bits in a device all the way to the management of the entire storage hierarchy in large enterprise and cloud settings. Studying many of the important issues in this entire spectrum often requires storage access traces from the storage server side, but these are often hard to come by. To address this gap, we present a method to generate synthetic traces using a novel generative adversarial network (GAN) architecture that captures the realism and diversity of real storage traces. The generated traces can be used to augment the existing workload traces of interest for a variety of storage system studies. We demonstrate how the proposed method can generate storage traces that have the overall characteristics of the real traces and yet provide behavioral diversity.