Dynamic Baseline Anomaly Detection for Data Center Metrics
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Solution Overview
Problem
Traditional monitoring systems in data centers face challenges in efficiently identifying data anomalies due to the increasing volume and finer-grained nature of metrics data, often resulting in false positives and inadequate detection of meaningful anomalies.
Innovation Solution
A method and system for continuous data anomaly detection that categorizes metrics data into non-overlapping time segments, performs statistical analysis, and dynamically generates ranges of acceptable metric values based on historical performance, minimizing false positives and improving over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional monitoring systems use fixed baselines or thresholds to analyze metrics data, then the system complexity remains low and ease of operation is maintained, but the system generates false positives and cannot adapt to increasing data volume and finer-grained data
Solution Approach 1:
The patent implements dynamic baselines that automatically adapt to changing data patterns over time. The system continuously learns from historical metrics data and adjusts baseline values dynamically, allowing the monitoring system to adapt to increasing data volume and finer-grained data without manual intervention. This resolves the contradiction by making the system adaptable while managing complexity through automation.
Solution Approach 2:
The system changes the parameter of baseline values from fixed static thresholds to dynamic adaptive parameters. By transforming the baseline parameter from a constant value to a dynamically calculated value based on historical data statistics, the system achieves adaptability to changing conditions while the underlying statistical methods keep the implementation complexity manageable.
2Measurement precision
If the number of applications and sampled data per application increases to provide more comprehensive monitoring coverage, then measurement precision and detection capability improve, but the quantity of data to be processed increases significantly
Solution Approach 1:
The patent extracts only the essential statistical features (mean, standard deviation, minimum, maximum) from the raw metrics data to create baseline values. By extracting these key statistical parameters rather than processing all raw data points, the system achieves precise anomaly detection while significantly reducing the computational burden associated with processing large volumes of fine-grained data.
Solution Approach 2:
The system performs statistical analysis on a representative sample of historical data to establish baselines, rather than continuously analyzing every data point. This partial action approach allows the system to achieve sufficient detection precision without the excessive computational cost of processing the entire data volume, balancing precision with data quantity management.
3Reliability
If fixed thresholds are used for anomaly detection, then the system is simple to implement and operate, but it produces false positives and misses meaningful anomalies in dynamic environments
Solution Approach 1:
The monitoring system performs self-service by automatically calculating and updating baseline values from historical data without requiring manual configuration or intervention. The system autonomously learns normal behavior patterns and adjusts detection thresholds dynamically, improving reliability while maintaining ease of operation through automation. This resolves the contradiction by making the system both reliable and easy to operate through self-configuration.
Solution Approach 2:
The system implements feedback mechanisms where detection results and performance metrics are continuously monitored and used to refine baseline calculations. This feedback loop improves anomaly detection reliability by continuously adapting to changing conditions, while the automated nature of the feedback process maintains ease of operation without requiring manual tuning or configuration.
Data Source
AI summary
A method for continuous data anomaly detection includes identifying a period of time covered by metrics data stored in a repository. The stored metrics data is categorized into a plurality of non-overlapping time segments. Statistical analysis of the stored metrics data is performed based on the identified period of time. A range of acceptable metric values is dynamically generated based on the performed statistical analysis.