IT Alert Action Recommendations Using Component Extraction
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Solution Overview
Problem
Existing IT systems face challenges in effectively processing events in complex and noisy environments, leading to inefficiencies in alert resolution and resource management.
Innovation Solution
A method and system that utilize a component extraction tool to identify IT components associated with alerts, and then provide recommended actions for alert resolution, using learning algorithms to update action-to-component likelihoods based on user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual techniques and pre-programmed rules are used to process alerts, then alert resolution can be achieved, but labor and computing intensity increases significantly
Solution Approach 1:
The system enables self-service by automatically extracting components from alerts and generating recommended actions without requiring manual analysis. The machine learning model autonomously processes alerts, identifies affected components, and suggests resolution actions, reducing the need for human intervention and manual processing.
Solution Approach 2:
The system implements feedback mechanisms where the quality of extracted components and generated actions is continuously improved through training on historical alert data. The machine learning model learns from past alert resolutions to enhance its component extraction and action recommendation accuracy over time.
2Adaptability or versatility
If pre-programmed rules are used for alert management, then basic alert processing is possible, but the system cannot adapt to changes in IT environments
Solution Approach 1:
The machine learning model performs self-service by automatically adapting to changes in the IT environment through continuous learning from historical data. It autonomously updates its understanding of component relationships and alert patterns without requiring manual reconfiguration of monitoring rules.
Solution Approach 2:
The system handles parameter changes by dynamically adjusting its alert processing behavior based on evolving IT environments. The machine learning model adapts to changing infrastructure, components, and alert patterns by learning from historical data, allowing the system to maintain effectiveness despite environmental changes.
3Reliability
If centralized IT Operations manages complex distributed systems, then coordination is possible, but computing resources are consumed excessively
Solution Approach 1:
The machine learning model enables self-service by performing intelligent alert processing and component extraction autonomously. This reduces the computational burden on centralized systems by distributing the intelligence to automated ML-based processing, thereby maintaining reliability while reducing overall computational resource consumption.
Data Source
AI summary
An alert triggered by an event in a managed information technology (IT) environment is received. An IT component associated with the alert is identified using a component extraction tool. A first set of recommended actions for the alert is output. A user-selected action for resolving the alert is received. Feedback data regarding whether the user-selected action resolved the alert is collected. Using learning algorithms, action-to-component likelihoods are updated based on the collected feedback data. Future action recommendations are modified based on the updated action-to-component likelihoods. The future action recommendations are stored in an actions library for subsequent alert resolutions.