Anomaly Detection in Multi-Tenant Database Systems
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Solution Overview
Problem
In multi-tenant database systems, it is challenging to manually detect and address anomalies such as sudden increases in CPU utilization, which can affect other organizations, due to the lack of automated systems capable of distinguishing between organic usage and unexpected surges, leading to infrastructure capacity issues and poor customer experience.
Innovation Solution
An anomaly detection system that monitors performance data for level shifts, slope changes, spikes, and load imbalances, prioritizes detected anomalies, and correlates them with known events to identify root causes, enabling automated remediation and capacity adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If manual monitoring of infrastructure performance is performed, then detailed performance data can be reviewed, but the system cannot scale to handle large quantities of data from multiple sources and experiences unexpected surges in demand
Solution Approach 1:
The system automatically monitors infrastructure performance metrics, detects anomalies, and generates remediation actions without requiring manual intervention. The anomaly detection system continuously collects and analyzes performance data from multiple sources, automatically identifying issues and proposing resolutions, thereby freeing operators from manual monitoring tasks while handling large volumes of data efficiently
Solution Approach 2:
The patent replaces manual mechanical monitoring processes with automated computational systems. Instead of human operators manually reviewing performance data, the system uses algorithms to automatically detect anomalies in performance metrics, substitute manual analysis with automated pattern recognition and statistical analysis, and generate remediation actions through computational logic
2Extent of automation
If automated anomaly detection is implemented, then manual infrastructure patrols can be reduced, but the system must accurately distinguish between organic usage and unexpected surges
Solution Approach 1:
The system establishes baseline performance metrics and thresholds in advance through historical data analysis. Before anomalies occur, the system pre-processes performance data to understand normal patterns, seasonal variations, and expected behavior, enabling accurate distinction between organic usage and actual anomalies when they occur
Solution Approach 2:
The anomaly detection system continuously compares current performance metrics against established baselines and historical patterns, providing feedback loops that automatically adjust detection sensitivity. The system learns from resolved anomalies and updates its understanding of normal behavior, improving accuracy over time while maintaining automated operation
3Reliability
If all detected anomalies are investigated, then root cause analysis can be performed, but the system generates false positives that waste time and resources
Solution Approach 1:
The system applies selective investigation by prioritizing anomalies based on their severity, impact, and likelihood of being false positives. Rather than investigating all detected anomalies equally, the system focuses resources on high-value anomalies that truly indicate problems, while filtering out obvious false positives through intelligent classification and confidence scoring
Solution Approach 2:
The system dynamically adjusts detection thresholds and anomaly sensitivity parameters based on contextual factors. By changing detection parameters according to time of day, seasonal patterns, and system state, the system reduces false positives during normal variations while maintaining high detection accuracy for genuine anomalies, thereby reducing wasted investigation time
Data Source
AI summary
System and methods are described for anomaly detection and root cause analysis in database systems, such as multi-tenant environments. In one implementation, a method comprises receiving an activity signal representative of resource utilization within a multi-tenant environment; detecting a plurality of anomalies in the activity signal; computing a priority score for each of the plurality of anomalies; correlating at least a subset of the plurality of anomalies to one or more performance metrics of the multi-tenant environment; and transmitting a remediation signal to one or more devices in the multi-tenant environment based on the correlations and the priority scores.