Dynamic Alarm Threshold Adjustment for Software Performance
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Solution Overview
Problem
Manual setting of performance thresholds for software applications leads to undetected changes in performance metrics, resulting in potential performance regressions and inefficiencies, as these changes are often missed or updated too slowly and are prone to human error.
Innovation Solution
Implement a system that dynamically updates alarm thresholds based on real-time performance metric analysis, automatically adjusting thresholds as performance changes, thereby preventing undetected regressions and reducing the likelihood of human error.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If performance thresholds are set manually by human operators, then initial performance monitoring can be established, but the thresholds become outdated quickly and fail to detect performance changes
Solution Approach 1:
The system implements automatic feedback loops where performance metric data is continuously collected, analyzed, and used to dynamically adjust thresholds. The processor monitors performance metrics and automatically updates thresholds based on detected changes, creating a closed-loop system that adapts to performance variations without human intervention.
Solution Approach 2:
The threshold management system performs self-service by automatically detecting performance changes and adjusting its own thresholds without requiring external human input. The system autonomously analyzes performance data, identifies trends, and modifies thresholds to match current performance levels, making the threshold maintenance process self-sufficient.
2Reliability
If manual threshold updates are performed, then some performance changes can be detected, but human error and oversight cause significant periods of undetected performance regressions
Solution Approach 1:
The system replaces the mechanical human-operated threshold update process with an automated computational system. The processor continuously analyzes performance metrics using algorithms to detect changes and adjust thresholds, eliminating human error and oversight while improving both reliability and measurement precision through consistent, error-free automated monitoring.
3Productivity
If performance thresholds are updated frequently to capture fast changes, then detection accuracy improves, but the complexity of managing and maintaining thresholds increases
Solution Approach 1:
The automated system handles the complexity of frequent threshold updates autonomously without requiring human management effort. The processor automatically analyzes performance data, determines when threshold adjustments are needed, and implements updates without human intervention, thereby increasing update speed while actually reducing operational complexity by eliminating manual management tasks.
Data Source
AI summary
Embodiments of the present disclosure relate to dynamically maintaining alarm thresholds for software application performance management. Other embodiments may be described and/or claimed.