用知识图谱增强大模型，自动分析固态硬盘运行问题
KORAL: Knowledge Graph Guided LLM Reasoning for SSD Operational Analysis
- 将碎片化日志与文献构建知识图谱，驱动大模型推理
- 实现描述性、预测性、处方性及假设分析的全流程诊断
- 生成可解释结论，降低人工依赖，适合运维与研发人员
固态硬盘（SSD）在数据中心、消费平台和关键系统中至关重要，但其性能与可靠性诊断困难，因数据分散且时间不连续。现有方法需大量数据与专家输入，仅提供有限洞察。退化不仅源于负载变化与架构演进，还受温度、湿度、振动等环境因素影响。本文提出KORAL，一种基于知识图谱引导的大语言模型推理框架，整合结构化知识图谱与大模型，实现对SSD运行的深度分析。KORAL从碎片化遥测数据构建数据知识图谱，并融合已组织的文献知识图谱，将非结构化信息转化为可查询图谱，使遥测数据变为结构化知识。两个图谱协同引导大模型，输出基于证据、可解释的分析结果，符合领域术语与约束。真实生产日志评估显示，KORAL可达成专家级诊断与建议，支持可验证解释，提升推理透明度，辅助操作决策，减少人工工作量，提升服务质量。据我们所知，这是首个结合大模型与知识图谱的端到端全谱系SSD分析系统，涵盖描述性、预测性、处方性及假设分析。我们开源了专用于SSD的知识图谱，以推动基于知识的存储系统研究的可复现性。
原文摘要 · Abstract (English)
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
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