R&D: Device-Level Optimization Techniques for SSDs, Survey

arXiv has published an article written by Tianyu Ren, The City University of Hong Kong, China, Yajuan Du, Wuhan University of Technology, China, Jinhua Cui, Huazhong University of Science and Technology, China, Yina Lv, Xiamen University, China, Qiao Li, and Chun Jason Xue, Mohamed bin Zayed University of Artificial Intelligence, The United Arab Emirates.

Abstract: “Solid-state drives (SSDs) have revolutionized data storage with their high performance, energy efficiency, and reliability. However, as storage demands grow, SSDs face critical challenges in scalability, endurance, latency, and security. This survey provides a comprehensive analysis of SSD architecture, key challenges, and device-level optimization techniques. We first examine the fundamental components of SSDs, including NAND flash memory structures, SSD controller functionalities (e.g., address mapping, garbage collection, wear leveling), and host interface protocols (SATA, SAS, NVMe). Next, we discuss major challenges such as reliability degradation, endurance limitations, latency variations, and security threats (e.g., secure deletion, ransomware defense). We then explore advanced optimization techniques, including error correction mechanisms, flash translation layer (FTL) enhancements, and emerging architectures like zoned namespace (ZNS) SSDs and flexible data placement (FDP). Finally, we highlight open research challenges, such as QLC/PLC NAND scalability, performance-reliability trade-offs, and SSD optimizations for AI/LLM workloads. This survey aims to guide future research in developing next-generation SSDs that balance performance, longevity, and security in evolving storage ecosystems.“