Managing Hybrid Solid-State Drives Using Large Language Models

TL;DR

This work tackles the challenge of optimizing hybrid SSDs that integrate multiple flash modes (e.g., SLC and QLC) by confronting a large, interdependent design space. It introduces LLM-hybridSSD, an auto-tuning framework that uses large language models to analyze hardware, system, and workload characteristics to generate and verify configurations, while also examining the limits of directly replacing ML-based strategies. The authors categorize hybrid-SSD parameters into SSD-specific, workload-related, and strategy-related groups, identify performance-sensitive parameters, and implement an LLM-based tuning workflow with prompt engineering, configuration generation, and mistake correction. Empirically, LLM-hybridSSD achieves substantial gains over default and ML-based baselines, including significant reductions in response time and write amplification, and discusses deployment trade-offs, cost, and future enhancements for integrating LLM-driven tuning into storage controllers and host systems.

Abstract

Hybrid Solid-State Drives (SSDs), which integrate several types of flash cells (e.g., single-level cell (SLC) and multiple-level cell (MLC)) in a single drive and enable them to convert between each other, are designed to deliver both high performance and high storage capacity. However, compared to traditional SSDs, hybrid SSDs also introduce a much larger design space, resulting in higher optimization complexity due to more design factors involved, including flash conversion timing and data migration between different flash cells, etc. To address these challenges, large language models (LLMs) could be a promising technique, as they excel in handling complex, high-dimensional parameter space exploration by leveraging their advanced capability to identify patterns and optimize solutions. Recent works have started exploring the use of LLMs to optimize computer systems. However, to the best of our knowledge, no study has focused on optimizing SSDs with the assistance of LLMs. In this work, we explore the potential of LLMs in understanding and efficiently managing hybrid SSD design space. Specifically, two important questions are exploited and analyzed: 1) Can LLMs offer optimization potential for Hybrid SSD management? 2) How to leverage LLMs for the performance and efficiency of hybrid SSD optimization? Based on the observations of exploration, we propose a comprehensive auto-tuning framework for hybrid SSDs, integrating LLMs to recommend customized configurations using calibration prompts derived from hardware, system, and workload information. Experimental results reveal a 62.35% improvement in throughput and a 57.99% decrease in write amplification compared to the default hybrid SSD configurations achieved with the incorporation of LLMs.

Figures (12)

• Figure 1: Core management module of (a) traditional SSD (b) host-managed SSD.
• Figure 2: Internal architecture of flash-based Hybrid SSDs.
• Figure 3: Workloads awareness with ML-based separator.
• Figure 4: Space management with RL-based agent.
• Figure 5: Overall performance and cost of LLM-based data hotness classification scheme.