Anomaly Detection Service for Metric Data Monitoring
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Solution Overview
Problem
Existing anomaly detection systems require significant user configuration and adaptation over time, making them complex and unsuitable for users who need to monitor changing data patterns, as they often rely on static thresholds that may not accurately reflect the evolving nature of the data.
Innovation Solution
An anomaly detection service that automatically creates and configures anomaly detection models based on user requests, allowing users to customize settings and provide domain knowledge, and updates models periodically using historical data and new algorithms, reducing the need for extensive user involvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If automatic anomaly detection models are implemented, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The anomaly detection service automatically performs model creation, configuration, and updates without requiring user intervention. The system self-configures detection parameters, automatically trains models on historical data, and continuously updates them based on new data patterns, eliminating the need for users to manually configure complex anomaly detection settings while maintaining high operational ease
Solution Approach 2:
The system pre-configures multiple anomaly detection models with different algorithms and parameters before they are needed. Historical data is pre-processed and stored in ready-to-use formats, and model training is performed in advance so that when anomaly detection is requested, the system can immediately deploy pre-configured models without requiring users to perform complex setup procedures
2Adaptability or versatility
If static thresholds are used for anomaly detection, then device complexity is reduced, but adaptability deteriorates
Solution Approach 1:
The system transitions from static anomaly thresholds to dynamic, adaptive thresholds that automatically adjust based on changing data patterns. Anomaly detection models are continuously retrained on new historical data, allowing the system to adapt to evolving normal behavior patterns and detect anomalies accurately even as the underlying data distribution changes over time
Solution Approach 2:
The system automatically modifies detection parameters including threshold values, time windows, and sensitivity levels based on analyzed data characteristics. Different anomaly detection algorithms with varying parameters are selected and configured automatically based on the specific data being monitored, enabling the system to adapt to different data types and anomaly patterns without manual intervention
3Measurement precision
If sophisticated anomaly detection techniques are implemented, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The system automatically selects and configures the most appropriate anomaly detection algorithms and parameters based on the characteristics of the input data. Users simply need to specify what data to monitor, and the service autonomously performs model selection, parameter optimization, and detection configuration, eliminating the need for users to understand complex anomaly detection techniques while maintaining high measurement precision through sophisticated algorithms
Data Source
AI summary
Techniques are described for an anomaly detection service for metric data collected by a data monitoring service of a service provider network. The anomaly detection service provides various graphical user interfaces (GUIs), public application programming interfaces (APIs), and other interfaces that enable users to specify metric data of interest to the user and for which the user desires the service to detect occurrences of anomalies. The selected metric data generally can correspond to any type of time series data collected by the data monitoring service and to which a user has access. Example types of metric data that can be monitored by an anomaly detection service include, but are not limited to, operational data generated by various components of a computer system, business data generated by various types of applications, and the like.