KORAL: Knowledge Graph Guided LLM Reasoning for SSD Operational Analysis

2026-02-10

AI Summary

This work addresses the challenges in SSD performance and reliability diagnosis arising from fragmented telemetry data, inconsistent temporal sequences, and the limited interpretability of existing methods that heavily rely on expert knowledge. We propose the first end-to-end, full-spectrum SSD analysis system that integrates large language models (LLMs) with a structured knowledge graph. By constructing a queryable graph that unifies fragmented telemetry data with domain-specific literature, our approach guides the LLM toward domain-aligned, interpretable reasoning. The system supports descriptive, predictive, prescriptive, and counterfactual inference, delivering expert-level diagnostics and actionable recommendations on real-world production data while providing traceable explanations. This significantly enhances decision transparency, reduces manual intervention, and we further release the first SSD-specific knowledge graph as an open-source resource.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Solid State Drives (SSDs) are critical to datacenters, consumer platforms, and mission-critical systems. Yet diagnosing their performance and reliability is difficult because data are fragmented and time-disjoint, and existing methods demand large datasets and expert input while offering only limited insights. Degradation arises not only from shifting workloads and evolving architectures but also from environmental factors such as temperature, humidity, and vibration. We present KORAL, a knowledge driven reasoning framework that integrates Large Language Models (LLMs) with a structured Knowledge Graph (KG) to generate insights into SSD operations. Unlike traditional approaches that require extensive expert input and large datasets, KORAL generates a Data KG from fragmented telemetry and integrates a Literature KG that already organizes knowledge from literature, reports, and traces. This turns unstructured sources into a queryable graph and telemetry into structured knowledge, and both the Graphs guide the LLM to deliver evidence-based, explainable analysis aligned with the domain vocabulary and constraints. Evaluation using real production traces shows that the KORAL delivers expert-level diagnosis and recommendations, supported by grounded explanations that improve reasoning transparency, guide operator decisions, reduce manual effort, and provide actionable insights to improve service quality. To our knowledge, this is the first end-to-end system that combines LLMs and KGs for full-spectrum SSD reasoning including Descriptive, Predictive, Prescriptive, and What-if analysis. We release the generated SSD-specific KG to advance reproducible research in knowledge-based storage system analysis. GitHub Repository: https://github.com/Damrl-lab/KORAL

Problem

• SSD reliability
• performance diagnosis
• fragmented telemetry
• environmental factors
• expert-level analysis

Innovation

• Knowledge Graph
• Large Language Model
• SSD Operational Analysis
• Explainable Reasoning
• Telemetry Integration