Dynamic Server Threshold Optimization for Alert Accuracy
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Solution Overview
Problem
Monitoring servers in an enterprise environment is challenging due to the large number of servers, varying operating systems, and applications, leading to arbitrary and subjective parameter monitoring and threshold settings, resulting in false alerts and undetected issues.
Innovation Solution
A system and method that identifies core server parameters and baseline thresholds enterprise-wide, optimizing them over time based on composite historical performance data and the importance of each parameter, ensuring consistent monitoring across all servers, eliminating subjective threshold settings and generating alerts for corrective action.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If individualized monitoring thresholds are set for each server, then flexibility in monitoring different server types is improved, but subjectivity and arbitrariness increase leading to false alerts
Solution Approach 1:
The patent transforms static, subjective threshold values into dynamic, data-driven thresholds by continuously analyzing historical performance data. Thresholds are automatically adjusted based on observed server behavior patterns, eliminating arbitrary manual settings while maintaining adaptability to different server types through learned performance characteristics.
Solution Approach 2:
The system implements continuous feedback loops where monitoring data from all servers is aggregated, analyzed, and used to refine threshold settings. This feedback mechanism allows the system to learn from actual server performance and automatically adjust thresholds to reduce false alerts while maintaining sensitivity to real issues.
2Measurement precision
If monitoring thresholds are set too low to detect all potential issues, then detection sensitivity is improved, but false alert generation increases
Solution Approach 1:
The system dynamically adjusts monitoring thresholds based on historical performance data and server-specific characteristics. By continuously learning from actual server behavior, the system optimizes threshold values to distinguish between normal performance variations and genuine issues, maintaining high detection sensitivity while minimizing false alerts.
Solution Approach 2:
The patent applies customized threshold settings to different server types, locations, and workloads based on their specific performance characteristics. Each server group receives locally optimized thresholds derived from its historical data, allowing sensitive detection for each context without generating false alerts from overly generic threshold settings.
3Reliability
If manual threshold adjustment is performed to reduce false alerts, then alert quality is improved, but underlying problems may go undetected
Solution Approach 1:
The system continuously monitors server performance and uses feedback from historical data to automatically adjust thresholds. This eliminates the need for manual intervention while maintaining both alert quality and problem detection accuracy, as the system learns to distinguish between normal variations and genuine issues based on patterns in the data.
Solution Approach 2:
The monitoring system performs self-optimization by automatically analyzing its own performance data and adjusting thresholds without human intervention. The system serves itself by learning from historical patterns and making autonomous decisions about threshold settings, ensuring both reliability and detection precision are maintained simultaneously.
4Quantity of substance
If comprehensive monitoring of all servers is implemented, then coverage is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal monitoring framework that applies the same core monitoring logic and threshold optimization approach across all server types and locations. This standardized system handles diverse servers through a single multi-functional platform, achieving comprehensive coverage without proportionally increasing complexity, as the same system adapts to different server characteristics.
Data Source
AI summary
The present invention provides for identifying the core server parameters to be monitored enterprise-wide and the baseline thresholds/limits for such parameters. The thresholds are dynamically optimized as the server environment evolves over time based on the composite historical performance of the servers in the enterprise. Moreover, each parameter's threshold is optimized in comparison to the thresholds of other core parameters that impact that specific parameter. In the event that the monitoring results in a threshold being met or exceeded alerts may be generated to designated personnel and appropriate corrective action taken.