Network Alert State Machine for Alarm Management
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Solution Overview
Problem
Current network monitoring systems face challenges in managing alarms, including customization limitations, high false-positive rates, and difficulties in accessing information for untriggered conditions, leading to alarm fatigue and inefficient anomaly management.
Innovation Solution
A network monitoring system that employs a finite state machine to track alerts and apply transition rules, allowing administrators to configure anomaly detection and alert parameters, thereby generating alarms only when specific conditions are met, and persisting state information for effective anomaly tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If administrators apply filters to a large set of available alarms, then alarm customization is improved, but the customization process becomes limited and cumbersome
Solution Approach 1:
The alarm management system is segmented into multiple independent state machines, each handling specific alarm types or conditions. This segmentation allows administrators to customize individual alarm behaviors without affecting the entire system, making customization more manageable and less cumbersome while maintaining high adaptability.
Solution Approach 2:
The system implements dynamic alarm state machines that can transition between different states (e.g., pending, active, acknowledged, resolved) based on real-time conditions. This dynamic approach enables flexible customization of alarm workflows and state transitions, allowing administrators to adapt alarm behavior to specific needs without rigid constraints.
2Loss of information
If the system generates alarms for detected conditions, then information availability is improved, but the false-positive rate increases and alarm fatigue occurs
Solution Approach 1:
The system performs preliminary analysis and evaluation of detected anomalies before generating alarms. State machines evaluate multiple conditions and parameters in advance, assessing the significance and likelihood of false positives. This preliminary action ensures that only genuinely important anomalies trigger alarms, maintaining high information availability while reducing false positives and preventing alarm fatigue.
Data Source
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AI summary
A system and computer-implemented method to manage alerts, wherein the method includes receiving anomaly triggers associated with detected anomalies, associating an anomaly trigger of the received anomaly triggers with an alert, tracking the alert using a state machine, determining whether to change a state of the state machine if a subsequent anomaly trigger associated with the alert is received or if a predetermined amount of time passes without receiving trigger information, and generating an alarm based on the state of the state machine.