Adaptive Flap Detection via Geometric Metric Analysis
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Solution Overview
Problem
Conventional techniques for detecting flapping in computer systems require manual setting of threshold values and time windows, fail to account for normal state changes, and do not detect fluctuations below the threshold, leading to incomplete monitoring and potential pre-failure issues.
Innovation Solution
A flapping detection component that calculates the combined length of lines representing metric values over time in a Cartesian coordinate system, determines a baseline length, and compares it to detect instability, allowing for adaptive detection without static thresholds and accounting for normal fluctuations, with a tunable sensitivity using a flap tolerance factor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional boolean threshold-based detection is used, then flapping detection can be implemented with simple logic, but it requires manual setting of threshold values and time windows which reduces adaptability
Solution Approach 1:
The system automatically determines threshold values and time window parameters by analyzing historical metric data and system behavior patterns, eliminating the need for manual configuration. The detection component self-adjusts to different systems by learning their normal operational characteristics and establishing system-specific baselines automatically.
Solution Approach 2:
The patent transforms the detection approach from using fixed manual thresholds to using dynamically calculated parameters based on statistical analysis of system metrics. Threshold values are derived from historical data distributions, standard deviations, and pattern recognition rather than being statically defined, allowing automatic adaptation to different system characteristics.
2Device complexity
If static threshold values are used for detection, then the detection method is simple to implement, but it fails to detect fluctuations below the threshold and normal state changes
Solution Approach 1:
The detection system uses dynamic, adaptive thresholds that adjust based on historical data and system behavior rather than static fixed values. The time window parameters and sensitivity levels are modified automatically based on observed system patterns, allowing the detection to adapt to changing conditions while maintaining simplicity in the core detection logic.
Solution Approach 2:
The patent adds temporal dimension to the detection by analyzing trends over time rather than single-point thresholds. It incorporates multiple dimensions including historical baseline comparisons, rate of change analysis, and duration of state changes, transforming a simple threshold check into a multi-dimensional assessment that improves precision without significantly increasing complexity.
3Ease of operation
If manual threshold configuration is required, then the detection approach is easier to understand and implement, but it increases operational complexity and requires expert knowledge
Solution Approach 1:
The system performs automatic parameter tuning and threshold determination without requiring operator intervention. It self-configures by analyzing system metrics, determining appropriate time windows, and establishing detection thresholds based on learned patterns, thereby reducing operational complexity while maintaining ease of deployment.
Solution Approach 2:
The system performs preliminary analysis during an initialization or learning phase to automatically establish baseline parameters, threshold values, and detection settings before actual flapping detection begins. This preliminary configuration eliminates the need for manual setup while simplifying subsequent operation.
4Reliability
If conventional detection methods are used, then they provide basic flapping detection, but they do not account for how long the system has been flapping or normal changes in state
Solution Approach 1:
The system incorporates feedback mechanisms that continuously monitor detection results and adjust parameters based on observed patterns. It tracks the duration of flapping states, learns what constitutes normal versus abnormal changes, and uses this information to refine detection accuracy over time, preventing both false positives and missed detections.
Solution Approach 2:
The patent collects and analyzes more data than the minimum required for basic detection, including historical trends, duration metrics, and contextual information about system behavior. This excessive data collection and analysis enables the system to distinguish normal changes from actual flapping while providing comprehensive information about flapping duration and patterns.
Data Source
AI summary
A method by one or more electronic devices to detect flapping in a computer system. The method includes obtaining a plurality of metric values indicating a state of the computer system at various times within a time window, determining a combined length of lines connecting time-wise consecutive points representing the plurality of metric values in a cartesian coordinate system, wherein a first axis of the cartesian coordinate system represents time and a second axis of the cartesian coordinate system represents a metric associated with the plurality of metric values, determining a baseline length based on a maximum metric value within the time window, a minimum metric value within the time window, and a length of the time window, and determining whether the computer system flapped during the time window based on a result of comparing the combined length to the baseline length.