Reinforcement Learning-Assisted Management for Convertible SSDs

Convertible SSDs, which allow flash cells to convert between different types of flash cells (e.g., SLC/MLC/TLC/QLC), are designed for achieving both high performance and high density. However, previous designs with two types of flash cells encounter a performance cliff degradation once the flash cel...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	2023 60th ACM/IEEE Design Automation Conference (DAC) s. 1 - 6
Hlavní autoři:	Wei, Qian, Li, Yi, Jia, Zhiping, Zhao, Mengying, Shen, Zhaoyan, Li, Bingzhe
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 09.07.2023
Témata:	Convertible SSD Degradation Design automation Flash memory Performance evaluation Q-learning Real-time systems Reinforcement learning Resource management Throughput
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	Convertible SSDs, which allow flash cells to convert between different types of flash cells (e.g., SLC/MLC/TLC/QLC), are designed for achieving both high performance and high density. However, previous designs with two types of flash cells encounter a performance cliff degradation once the flash cells of single bit mode are consumed. In this work, we propose a novel level-based convertible SSD (e.g., including SLC-MLC-QLC), named RL-cSSD, that adopts an intermediate layer (e.g., MLC) as a performance cushion. A reinforcement learning-assisted device management scheme is designed to coordinate the data allocation, garbage collection and flash conversion processes considering both the SSD internal status and workload patterns. We evaluated RL-cSSD with various real-world workloads based on simulation. The experimental results show that the proposed RL-cSSD provides 72.98% higher performance on average compared with state-of-the-art schemes.
DOI:	10.1109/DAC56929.2023.10247929