LLM Incident Resolution for High-Volume Computing Environments
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Solution Overview
Problem
In complex big data environments, detecting and responding to incidents is challenging due to their inherent complexity and the sheer volume and velocity of data, leading to potential delays in addressing issues that can cause downtime, compromised data integrity, or security breaches.
Innovation Solution
A system utilizing a large language model (LLM) to generate and analyze incident records, detect similar incidents, and initiate adapted remediation actions based on predefined templates and historical data, leveraging vectorization and similarity scoring to identify effective responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If automated incident detection tools and monitoring systems are deployed to handle large volumes of data, then incident detection capability is improved, but system complexity increases
Solution Approach 1:
The patent introduces an LLM-based mediation layer between incident detection tools and human operators. The LLM processes complex incident data, generates structured incident records, and provides remediation recommendations, thereby simplifying the interaction between monitoring systems and human decision-makers without reducing detection capability
Solution Approach 2:
The system creates simplified representations of complex incidents through LLM-generated incident records that capture essential information in a standardized format. These records serve as copies that preserve incident meaning while reducing complexity for human consumption and automated processing
2Quantity of substance
If the volume and velocity of data increase in big data environments, then data processing capability is improved, but incident response time deteriorates
Solution Approach 1:
The LLM continuously processes incident data and generates remediation recommendations in advance, so that when incidents occur, ready-to-execute remediation actions are immediately available. This preliminary processing of data patterns and potential solutions reduces response time despite high data velocity
Solution Approach 2:
The system enables automated self-service incident response where the LLM autonomously analyzes incidents, generates remediation actions, and executes them without human intervention. This self-service capability maintains fast response times even as data volume increases
3Ease of operation
If manual incident analysis and response processes are used, then operational flexibility is improved, but productivity deteriorates
Solution Approach 1:
The system dynamically adjusts between automated LLM-driven response and manual human intervention based on incident complexity and context. The LLM handles routine incidents automatically to maintain high productivity, while human operators remain available for complex situations requiring operational flexibility
Solution Approach 2:
The system incorporates feedback loops where operational outcomes are fed back into the LLM training data, allowing the automated system to learn from human operations and improve its productivity while maintaining flexibility for edge cases
Data Source
AI summary
A system and method for initiating remediation actions in a computing environment based on similar incident detection is presented. The method includes: generating a first incident record, based on a correlated plurality of event records, each event record indicating an event in a computing environment; generating a first vector based on the first incident record; detecting a similar incident record based on the first vector and a corresponding vector of the similar incident record; detecting a remediation action associated with the similar incident record, the remediation action previously executed in the computing environment; generating an adapted remediation action based on data extracted from the first incident record; and initiating the adapted remediation action in the computing environment.