Alert Cluster Analysis for Reducing Alert Overload
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Solution Overview
Problem
Existing alert management systems generate excessive volumes of alerts, leading to alert overload and fatigue, which can result in errors and potential service outages, particularly in service-oriented platforms with numerous interdependent services and microservices.
Innovation Solution
Implement an alert cluster analysis apparatus that groups alerts into clusters based on features using a clustering model, determines significance scores, and outputs policy change recommendations when certain clusters satisfy an insignificance threshold, allowing for bulk actions and policy adjustments to reduce alert volume.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If alert management systems generate comprehensive alerts to ensure all service issues are detected, then detection reliability is improved, but alert volume increases causing operator fatigue and errors
Solution Approach 1:
The patent segments alerts into clusters based on similarity in features such as service name, error type, and temporal proximity. This segmentation allows operators to view consolidated alert clusters rather than individual alerts, reducing the quantity of visible alerts while maintaining detection reliability through structured organization.
Solution Approach 2:
The patent merges similar alerts into unified clusters by identifying common patterns and characteristics. Multiple alerts with similar features are combined into a single alert cluster representation, reducing alert volume while preserving the information content and reliability of issue detection.
2Ease of operation
If alerts are grouped into clusters to reduce alert volume, then ease of operation is improved, but complexity of alert processing increases
Solution Approach 1:
The patent implements self-service through automated clustering algorithms that automatically group alerts based on predefined features and patterns. The system performs feature extraction, similarity calculation, and cluster formation autonomously, reducing the need for manual intervention and simplifying operator tasks while managing processing complexity through automation.
Solution Approach 2:
The patent changes parameters by transforming raw alert data into extracted features (such as service identifiers, error codes, temporal patterns) that are used for clustering. This parameter transformation simplifies the alert processing logic by working with normalized feature representations rather than raw alert messages.
3Measurement precision
If feature extraction models are applied to alert clustering, then alert significance scoring accuracy is improved, but computational resources and processing time increase
Solution Approach 1:
The patent applies partial action by extracting only the most relevant features from alerts (such as service name, error type, severity level) rather than all possible alert attributes. This selective feature extraction maintains sufficient accuracy for significance scoring while reducing computational resource consumption compared to processing all alert data.
Solution Approach 2:
The patent replaces complex manual analysis mechanisms with automated machine learning models that perform feature extraction and significance scoring computationally. This substitution reduces the need for complex processing logic and manual intervention, optimizing the balance between scoring accuracy and computational efficiency.
Data Source
AI summary
Various embodiments disclosed herein are directed to a system, method, apparatus, and/or a computer program product that are configured to programmatically analyze alert clusters and make recommendations to alert managers as to alert policy changes that might reduce alert volume without hindering the intended performance of the alert management system. For example, various embodiments are configured to programmatically determine if a threshold number of alerts within an alert cluster are insignificant and output an alert policy change recommendation interface that is configured to allow alert mangers to execute adjustments to underlying alert policies. In some embodiments, alert policy changes are recommended only when a threshold number of alerts within an alert cluster are insignificant and such threshold number of alerts are determined to be increasing within the monitored software application framework.