Learning-based Data Separation for Write Amplification Reduction in Solid State Drives

Abstract: Garbage collection in SSDs causes write amplification. The key to mitigating this problem is separating data by lifetime. Prior works proposed using machine learning to accurately predict data lifetime but prediction is performed at the host side, burdening the host storage stack. We present PHFTL, a practical, holistic FTL design with device-side learning-based data separation. The machine learning model in PHFTL accurately and adaptively predicts the lifetime of every written page. A suite of enabling techniques are introduced to keep computation and storage overhead low. Extensive evaluation of PHFTL demonstrates superiority over state-of-the-art and feasibility on real hardware.