Unsupervised Learning for Misbehaving Component Detection
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Solution Overview
Problem
Large systems, such as e-commerce and social media platforms, face challenges in identifying misbehaving components due to their complexity and dynamic workloads, making it difficult to monitor high-dimensional data points and set appropriate threshold values for anomaly detection, which often relies on unsuitable methods with high computation complexity and overhead.
Innovation Solution
A method involving a behavior-learning computing device that performs unsupervised machine learning to identify states and state transitions of system components during a training phase, generating behavior models, and an anomaly-detecting computing device that uses these models to detect deviations in real-time data points during a detection phase, thereby identifying anomalies in system components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional anomaly detection methods with threshold values are used, then anomaly detection can be performed, but the computation complexity and overhead are high
Solution Approach 1:
The system performs unsupervised machine learning during a training phase to pre-identify states and state transitions of system components before actual anomaly detection. This preliminary action creates behavior models that capture normal system behavior patterns, allowing the detection phase to simply compare real-time data against these pre-established models rather than performing complex computations on the fly.
Solution Approach 2:
The system creates simplified copies of system behavior in the form of behavior models that represent normal operation. These models capture the essential states and transitions of components without containing all the complexity of the actual system. During detection, the system compares real data against these simplified behavioral copies, dramatically reducing computational requirements while maintaining detection accuracy.
2Reliability
If thousands of metrics and anomaly alerts are monitored, then system failures can be detected, but false positives increase and system complexity increases
Solution Approach 1:
The system segments the monitoring task by identifying and tracking only the critical states and state transitions of system components. Instead of monitoring all possible metrics simultaneously, the unsupervised learning process divides the complex monitoring space into distinct behavioral states, allowing the system to focus detection efforts on transitions between these specific states rather than analyzing thousands of individual metrics.
Solution Approach 2:
The system transforms the monitoring approach by changing from fixed threshold parameters to dynamically learned state parameters. The unsupervised machine learning automatically identifies relevant parameters and their valid ranges based on normal system behavior, replacing static thresholds with adaptive state-based parameters that reduce false positives while maintaining detection sensitivity.
3Measurement precision
If unsupervised machine learning is performed to identify states and transitions, then anomaly detection precision improves, but training phase computation time increases
Solution Approach 1:
The system performs the computationally intensive unsupervised machine learning during a preliminary training phase that occurs before the system goes into production. This training phase captures normal behavioral patterns and stores them as behavior models. Once trained, the system enters a lightweight detection phase that simply compares real-time data against the pre-learned models, making the ongoing operational overhead minimal despite the initial training investment.
Data Source
AI summary
A method or apparatus for monitoring a system by detecting misbehaving components in the system is presented. A computing device receives historical data points based on a set of monitored signals of a system. The system has components that are monitored through the set of monitored signals. For each monitored component, the computing device performs unsupervised machine learning based on the historical data points to identify expected states and state transitions for the component. The computing device identifies one or more steady components based on the identified states of the monitored components. The computing device also receives real-time data points based on monitoring the set of signals from the system. For each identified steady component, the computing device examines the received real-time data points for deviation from the expected state and state transitions of the steady component. The computing device reports anomaly in the system based on the detected deviations.