Automated Anomaly Detection Framework for Grid Resources
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Solution Overview
Problem
Manual monitoring of metrics for system performance and health becomes increasingly difficult due to complications and delays in data generation, leading to potential significant losses if anomalies are not identified in a timely manner.
Innovation Solution
An automated anomaly detection system that generates a scheduling map based on system processes and estimated running times, allowing for the monitoring and detection of anomalies in metrics, including erroneous and missing data, using a workflow data structure and directed graphs to predict data availability and detect abnormalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual monitoring of metrics is performed, then system health can be monitored, but detection efficiency is low and delays occur
Solution Approach 1:
The system automatically monitors its own metrics and detects anomalies without external intervention. The automated anomaly detection system continuously collects, processes, and analyzes metrics from multiple data sources, performing self-diagnosis and self-monitoring functions that eliminate the need for manual oversight while maintaining high detection accuracy.
Solution Approach 2:
The patent replaces manual monitoring mechanisms with automated computational systems. Instead of human operators manually checking metrics, the system uses automated data collection, processing, and analysis pipelines that continuously monitor system health, detect anomalies, and generate alerts, thereby eliminating detection delays associated with manual processes.
2Productivity
If automated anomaly detection is implemented, then detection efficiency is improved, but system complexity increases
Solution Approach 1:
The automated anomaly detection system is divided into distinct modular components: data collection modules that gather metrics from various sources, data processing modules that clean and transform the data, analysis modules that detect anomalies using statistical or machine learning methods, and notification modules that alert operators. This segmentation allows each component to be independently developed, maintained, and optimized, reducing overall system complexity while maintaining high detection efficiency.
Solution Approach 2:
The system employs universal data processing and analysis components that can handle multiple types of metrics and data sources through standardized interfaces and protocols. The anomaly detection algorithms are designed to work across different system contexts and metric types, reducing the need for custom-built solutions for each specific monitoring scenario and thereby simplifying the overall system architecture.
3Measurement precision
If comprehensive metrics monitoring is performed, then anomaly detection accuracy is improved, but data processing complexity increases
Solution Approach 1:
The system performs preliminary data processing and validation before anomaly detection analysis. Metrics are collected, cleaned, transformed, and pre-processed in standardized formats before being passed to the anomaly detection algorithms. This preliminary action ensures that the data is ready for analysis in a consistent manner, reducing the complexity of the actual anomaly detection process while maintaining high detection accuracy through comprehensive metric monitoring.
Data Source
AI summary
In one embodiment, a workflow data structure may be generated, updated, or obtained. The workflow data structure may represent system processes, relationships among the system processes, data input to the system processes, data generated by the system processes, and estimated running times associated with the system processes, wherein the data generated by the system processes includes a plurality of metrics. A scheduling map may be generated or updated based, at least in part, on the relationships among the system processes and the estimated running times associated with the system processes, where the scheduling map indicates estimated times at which the metrics are anticipated to be available. The metrics may be monitored based, at least in part, on the scheduling map. Anomalies may be detected according to a result of monitoring the metrics.