Device-Level Optimization Techniques for Solid-State Drives: A Survey
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866910018263056384 |
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| author | Ren, Tianyu Du, Yajuan Cui, Jinhua Lv, Yina Li, Qiao Xue, Chun Jason |
| author_facet | Ren, Tianyu Du, Yajuan Cui, Jinhua Lv, Yina Li, Qiao Xue, Chun Jason |
| contents | 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. Next, we discuss major challenges such as reliability degradation, endurance limitations, latency variations, and security threats. 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 the development of next-generation SSDs that balance performance, endurance, and security in evolving storage ecosystems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_10573 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Device-Level Optimization Techniques for Solid-State Drives: A Survey Ren, Tianyu Du, Yajuan Cui, Jinhua Lv, Yina Li, Qiao Xue, Chun Jason Hardware Architecture 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. Next, we discuss major challenges such as reliability degradation, endurance limitations, latency variations, and security threats. 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 the development of next-generation SSDs that balance performance, endurance, and security in evolving storage ecosystems. |
| title | Device-Level Optimization Techniques for Solid-State Drives: A Survey |
| topic | Hardware Architecture |
| url | https://arxiv.org/abs/2507.10573 |