Device-Level Optimization Techniques for Solid-State Drives: A Survey

2025-07-10

AI Summary

To address critical challenges in scalability, endurance, latency, and security of SSDs, this paper presents a systematic survey of co-optimization mechanisms across NAND flash device structures, controller architectures, and host interface protocols (SATA/SAS/NVMe). It analyzes device-level techniques—including error correction, flash translation layer (FTL) design, garbage collection, and wear leveling—and provides the first comprehensive review of adaptation strategies for emerging architectures such as Zoned Namespaces (ZNS) SSDs and the Flexible Data Placement (FDP) standard. Furthermore, it introduces a performance-reliability trade-off framework for QLC/PLC NAND under AI and large-model workloads, identifying key cross-layer research gaps spanning interfaces, media, and algorithms. The study delivers both theoretical foundations and a concrete technology roadmap for developing next-generation intelligent storage systems that are highly reliable, low-latency, and security-enhanced.

Technology Category

• Search and Optimization: Distributed Search
• Machine Learning: Hardware-aware ML
• Multiagent Systems: Agent/AI Theories and Architectures

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Addressing SSD challenges in scalability, endurance, latency, and security
• Exploring optimization techniques for NAND flash memory and SSD controllers
• Investigating future SSD improvements for AI and large language models

Innovation

• Error correction mechanisms for SSD reliability
• Flash translation layer (FTL) enhancements
• Zoned namespace (ZNS) SSDs architecture