Automated Network RCA Using LLMs for Incident Remediation
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Solution Overview
Problem
Current root cause analysis (RCA) methods in network management rely heavily on manual processes and human expertise, which are time-consuming, error-prone, and difficult to scale in complex environments, hindering effective resolution of network issues.
Innovation Solution
Implementing intelligent network incident management systems that utilize trained Large Language Models (LLMs) for automated RCA and remediation, leveraging tenant-specific metrics, graphs, and historic logs to identify and address network issues with automated remediation scripts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual processes and human expertise are used for root cause analysis, then accuracy in identifying network issues can be maintained through human judgment, but the process becomes time-consuming and difficult to scale
Solution Approach 1:
The patent replaces manual mechanical analysis processes with an automated AI system that uses machine learning models to analyze network metrics, logs, and performance data. The system automatically identifies root causes by processing network data through trained algorithms, eliminating the need for manual human analysis while maintaining or improving accuracy through consistent pattern recognition across large datasets.
Solution Approach 2:
The system enables self-service automated remediation where the AI-generated insights directly trigger remediation actions without human intervention. The closed-loop system automatically implements fixes based on identified root causes, allowing the network management system to service itself and reducing dependency on human operators for both analysis and remediation.
2Adaptability or versatility
If manual root cause analysis processes are used, then flexibility in handling complex network scenarios can be maintained through human expertise, but the process becomes error-prone and difficult to scale
Solution Approach 1:
The patent replaces manual mechanical analysis processes with an automated AI system that uses machine learning models to analyze network metrics, logs, and performance data. The system automatically identifies root causes by processing network data through trained algorithms, eliminating the need for manual human analysis while maintaining or improving accuracy through consistent pattern recognition across large datasets.
Solution Approach 2:
The system implements feedback loops where remediation outcomes are continuously monitored and fed back into the training data for the AI models. This closed-loop feedback mechanism allows the system to learn from past performance, continuously improving its accuracy and adaptability while maintaining reliable automated operation without human intervention.
3Duration of action of stationary object
If automated remediation is implemented, then service continuity can be improved by reducing downtime, but the system complexity increases
Solution Approach 1:
The patent segments the complex automated remediation system into distinct functional modules: data collection from network sources, AI model analysis for root cause identification, remediation action generation, and outcome monitoring. This modular segmentation manages system complexity by organizing functions into independent, manageable components while maintaining the overall automated service continuity capability.
Solution Approach 2:
The AI system is designed as a universal platform that handles multiple network analysis and remediation functions through a single integrated architecture. The same machine learning models process various types of network data (metrics, logs, performance indicators) and generate appropriate remediation actions across different network scenarios, reducing overall system complexity through multi-functionality rather than requiring separate specialized systems for each function.
Data Source
AI summary
Systems and methods for intelligent network incident management, Root Cause Analysis (RCA), and automated remediation include receiving metrics, graphs, and historic logs associated with network performance of a tenant of the cloud system; identifying a network issue based on the received metrics, graphs, and historic logs; performing an automated RCA to determine a cause of the network issue; and remediating the identified network issue based on the determined cause. Various embodiments include training specialized Large Language Models (LLMs) for performing the automated incident identification, RCA, and remediation.