Managing Hybrid Solid-State Drives Using Large Language Models

2025-03-17

AI Summary

To address the high-dimensional configuration optimization challenge in hybrid solid-state drives (SSDs) arising from dynamic SLC/MLC conversion and inter-cell data migration, this paper pioneers the integration of large language models (LLMs) into storage hardware management, proposing a hardware-aware, prompt-driven automated tuning framework. The method generates calibrated prompts by jointly encoding hardware architecture, real-time system state, and workload characteristics; it further incorporates performance modeling and feedback-driven fine-tuning to enable design-space-aware, end-to-end configuration recommendations. Experimental results show a 62.35% throughput improvement and a 57.99% reduction in write amplification over default configurations. The core contribution lies in establishing a novel LLM-empowered paradigm for storage hardware optimization—overcoming the limitations of conventional heuristic approaches in complex hybrid SSD scenarios and enabling adaptive, context-aware configuration tuning.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Algorithm Configuration
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Large language models for search

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.

Problem

• Optimizing hybrid SSD performance using large language models.
• Exploring LLMs for managing complex hybrid SSD design space.
• Developing an auto-tuning framework for hybrid SSDs with LLMs.

Innovation

• LLMs optimize hybrid SSD configurations.
• Auto-tuning framework integrates hardware, system data.
• LLMs improve throughput, reduce write amplification.