KORAL: Knowledge Graph Guided LLM Reasoning for SSD Operational Analysis
Mayur Akewar, Sandeep Madireddy, Dongsheng Luo, Janki Bhimani

TL;DR
KORAL is a novel framework combining Large Language Models with structured Knowledge Graphs to provide explainable, evidence-based analysis of SSD performance and reliability, reducing manual effort and improving decision-making.
Contribution
KORAL introduces an integrated approach that combines LLMs with domain-specific Knowledge Graphs for comprehensive SSD operational reasoning, a first in the field.
Findings
Delivers expert-level diagnosis and recommendations.
Provides grounded, explainable insights that enhance reasoning transparency.
Reduces manual effort and guides operator decisions.
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.…
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Taxonomy
TopicsAdvanced Data Storage Technologies · Caching and Content Delivery · Software System Performance and Reliability
