Network Fault Monitoring With Uncertainty-Driven Data Augmentation
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Solution Overview
Problem
Conventional monitoring systems for computing systems face challenges in accurately predicting faults with minimal resource consumption, often resulting in either over-sensitivity to normal network variability or missing critical issues due to static thresholds and reliance on large amounts of data processing.
Innovation Solution
A system that dynamically adjusts data retrieval based on uncertainty levels, using a single machine learning model to minimize resource usage by obtaining a smaller initial data set and augmenting it only when necessary to confirm fault conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a low threshold is used for fault detection, then sensitivity to faults is improved, but false positives increase overwhelming operators
Solution Approach 1:
The system dynamically adjusts the threshold based on learned patterns from historical data rather than using a fixed threshold. The machine learning model adapts to normal network variability and only triggers alerts when anomalies are detected with high confidence, resolving the contradiction between sensitivity and false positive rate.
Solution Approach 2:
The system changes the threshold parameter dynamically based on contextual factors learned from data. Instead of a static threshold, the system adjusts sensitivity parameters according to the specific network conditions and patterns observed, allowing high sensitivity while maintaining low false positive rates through context-aware parameter adjustment.
2Reliability
If a high threshold is used for fault detection, then false positives are reduced, but critical faults may be missed
Solution Approach 1:
The system uses dynamic threshold adjustment through machine learning to maintain high reliability while preserving fault detection capability. The model learns from historical data to set context-appropriate thresholds that prevent false positives while still detecting critical faults, avoiding the need for overly high static thresholds.
3Measurement precision
If large amounts of data are processed to improve fault prediction accuracy, then detection precision is improved, but resource consumption increases
Solution Approach 1:
The system extracts only the most relevant features and data points needed for fault detection rather than processing all available data. The machine learning model identifies and processes only the critical parameters that contribute to accurate fault prediction, significantly reducing computational resource consumption while maintaining high detection accuracy.
Solution Approach 2:
The system processes a partial set of data - specifically, only the most relevant features and parameters - rather than all available data. This partial processing approach achieves sufficient accuracy for fault detection while minimizing resource consumption by avoiding unnecessary data processing.
4Reliability
If multiple machine learning models are used to improve detection coverage, then fault detection capability is improved, but system complexity increases
Solution Approach 1:
The system uses a single machine learning model that is trained to detect multiple types of faults across different network conditions. This universal model approach provides comprehensive detection coverage without the complexity of managing multiple specialized models, as one model performs the functions of what would otherwise require several separate models.
Data Source
AI summary
Systems and methods for monitoring emerging faults at a network system are disclosed herein. The system may receive, such as from tool-based monitoring systems, data including measurements and testing results. The system may input an operational metric dataset into a fault detection machine learning model to obtain an indication of and degree of the fault condition at the network system. Responsive to determining that the degree of the fault condition indicates uncertainty of whether fault has occurred, the system may identify second commands. Based on transmitting the second commands, additional parameters may be obtained for generating an augmented dataset. The system may input the augmented dataset into the fault detection machine learning model to obtain an updated indication of the fault condition and updated degree of the fault condition and transmit an alert to an operator device.