Automated Training Data Generation for Distributed System Anomaly Prediction
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Solution Overview
Problem
Current systems face challenges in generating sufficient, accurately annotated training data for machine-learning components to predict anomalous behaviors in distributed computer systems, and existing methods are often opaque, making it difficult for administrators to trust their outputs.
Innovation Solution
The system automatically generates training data by labeling metric data using categorical KPI values and employs machine-learning components to predict operational anomalies, allowing for continuous training without human expert annotation and providing explainable decision trees and rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine-learning components are used to predict anomalous behaviors, then prediction capability is improved, but the need for accurately annotated training data increases
Solution Approach 1:
The system automatically generates and annotates its own training data using unsupervised anomaly detection algorithms. The anomaly detection component analyzes metric data and automatically labels anomalies without human intervention, creating a self-sustaining training data generation process that eliminates dependency on external annotated datasets.
Solution Approach 2:
The system performs preliminary unsupervised analysis of metric data to identify potential anomalies before using them as training labels. By pre-processing the metric data through anomaly detection algorithms, the system prepares training data in advance, reducing the need for extensive manual annotation efforts later.
2Reliability
If machine-learning components are deployed, then operational anomaly prediction is improved, but system complexity increases
Solution Approach 1:
The system divides the monitoring architecture into distinct modular components: metric data collection, unsupervised anomaly detection, training data generation, machine learning model training, and anomaly prediction. Each component performs a specific function and can be independently configured, maintained, and replaced, reducing overall system complexity despite the sophisticated capabilities.
Solution Approach 2:
The unsupervised anomaly detection component serves as an intermediary between raw metric data and the machine learning model. It bridges the gap by automatically generating training labels from unlabeled metric data, eliminating the need for complex manual annotation pipelines and simplifying the data preparation process.
3Measurement precision
If manual annotation methods are used, then training data accuracy is improved, but time consumption increases
Solution Approach 1:
The system replaces manual human annotation (mechanical process) with automated unsupervised anomaly detection algorithms. These algorithms analyze metric data patterns, identify anomalies based on statistical deviations, and automatically generate labels, substituting the time-consuming manual process with computational automation that maintains consistent accuracy.
Solution Approach 2:
The system implements a feedback loop where the machine learning model's predictions are continuously evaluated against actual system behavior. When discrepancies are detected, the system automatically adjusts training data generation parameters and re-trains models, creating a self-correcting process that maintains annotation accuracy without manual intervention.
4Productivity
If automated training data generation is implemented, then productivity is improved, but transparency of decision-making decreases
Solution Approach 1:
The system allows administrators to configure and adjust key parameters of the unsupervised anomaly detection algorithms, such as sensitivity thresholds, time window sizes, and anomaly scoring weights. By making these parameters可调 (adjustable) and interpretable, the system maintains transparency in how training data is generated while preserving automated efficiency.
Data Source
AI summary
The current document is directed to methods and systems that automatically generate training data for machine-learning-based components used by a metric-data processing-and-analysis component of a distributed computer system, a subsystem within a distributed computer system, or a standalone metric-data processing-and-analysis system. The training data sets are labeled using categorical KPI values. The machine-learning-based components are applied to metric data both for predicting anomalous operational behaviors and problems within the distributed computer system and for determination of potential causes of anomalous operational behaviors and problems within the distributed computer system. Training of machine-learning-based components is carried out concurrently and asynchronously with respect to other metric-data collection, aggregation, processing, storage, and analysis tasks.