Optimal SSD Management with Predictions

Recently, flash-based solid state drives (SSDs) have become a primary storage solution due to their advantages over hard-disk drives. Nonetheless, SSD management presents unique challenges. First, SSDs update data by writing a new copy to a clean slot, rather than overwriting old data. Second, SSDs support cleaning of entire blocks, while slots cannot be cleaned individually. Third, when cleaning a block, any valid data it stores must first be rewritten to another location, which adversely affects the SSD endurance and throughput. In this work, we address the SSD management problem, where the objective is to minimize the number of rewrites in SSDs. We approach the problem from a theoretical perspective, analyzing algorithms in a worst-case fashion. Motivated by recent advances in machine learning, we consider a learning-augmented setting where the algorithm has access to a predictive oracle, with performance guarantees expressed as a function of the error in the oracle’s output. Our main contribution is Gladiator, a novel online algorithm that optimally leverages predictions to enhance the performance of SSDs. Compared to prior work, Gladiator requires no additional non-volatile memory and is less sensitive to prediction errors. We further demonstrate the effectiveness of Gladiator under reasonable assumptions on the input distribution, and extend it to an extremely efficient offline algorithm. Empirical results confirm its superiority over state-of-the-art practical solutions across diverse SSD workloads.