LLM-Based Fault Cause Identification for Virtualized IT Systems
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Solution Overview
Problem
Identifying fault causes in complex IT systems, particularly in virtualized environments, is challenging due to operational complexity and the inflexibility of existing methods.
Innovation Solution
A fault cause identification support device that utilizes a large-scale language model (LLM) to analyze error messages, configuration information, and device states by generating question sentences based on these inputs to identify potential fault causes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional fault detection methods are used in virtualized IT systems, then fault detection capability is maintained, but fault cause identification becomes inflexible and difficult due to system complexity
Solution Approach 1:
The patent introduces an intermediary system comprising a question generation unit and a large-scale language model that acts as a mediator between the error message acquisition and fault cause identification. This intermediary translates technical error messages and system states into natural language questions, leveraging the language model's capabilities to provide flexible and accurate fault cause identification without requiring complex rule-based systems tailored to each virtualized environment.
2Measurement precision
If complex analysis methods are implemented to identify fault causes in large-scale IT systems, then identification accuracy improves, but analysis time and processing overhead increase
Solution Approach 1:
The patent replaces traditional mechanical rule-based analysis systems with a large-scale language model that processes fault information through natural language understanding. Instead of implementing complex mechanical analysis workflows and rule engines, the system substitutes these with an AI language model that can rapidly analyze error messages, configuration information, and device states to identify fault causes, significantly reducing analysis time while maintaining or improving accuracy.
3Adaptability or versatility
If predefined rules and methods are used for fault analysis, then analysis process is standardized, but the system cannot adapt to diverse fault scenarios in virtualized environments
Solution Approach 1:
The patent implements a universal fault analysis system based on a large-scale language model that can handle multiple fault scenarios and diverse IT system configurations through a single platform. The system acquires error messages, configuration information, and device states, then uses the language model's universal natural language processing capabilities to adapt to various fault types, virtualization environments, and system architectures without requiring separate rule sets or analysis methods for each scenario.
Data Source
AI summary
In a fault cause identification support device, an error message acquisition means acquires an error message from an IT system. A configuration information acquisition means acquires configuration information of the IT system. A device state acquisition means acquires state information of each of devices forming the IT system based on the error message and the configuration information. A question sentence generation means generates a first question sentence including the error message, the configuration information, the state information, and an instruction sentence for instructing analysis of an error cause. A response means inputs the first question sentence to a large-scale language model and acquire one or more candidates of the error cause as an answer. Thus, the fault cause identification support device capable of supporting in identifying a cause of a fault in the IT system is provided.