Queue Depth Metrics for Storage Performance Impact Detection
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Solution Overview
Problem
Conventional methods for monitoring and diagnosing data storage system issues require significant training and experience, and often rely on visual detection of subtle trends, which can be ineffective for identifying performance impact events.
Innovation Solution
A method using queue depth metrics derived from I/O operations per second (IOPS) and latency data to identify performance impact events, involving threshold comparisons and correlation analysis to detect and address potential issues automatically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional visual monitoring methods are used to display operating statistics, then system operation trends can be observed, but significant training and experience are required to spot and diagnose issues
Solution Approach 1:
The patent introduces an automated performance impact event detection system that acts as an intermediary between raw performance data and human administrators. This system computes queue depth metrics, performs correlation analysis, and automatically identifies performance impact events, eliminating the need for administrators to manually interpret complex visual trends while maintaining high detection accuracy
Solution Approach 2:
The patent replaces the manual visual inspection mechanism with an automated computational system. Instead of human administrators visually analyzing graphs and statistics, the system automatically computes queue depth metrics, performs statistical analysis, and generates alerts, substituting human cognitive processing with algorithmic analysis
2Productivity
If automated performance impact event detection is implemented, then issue identification efficiency is improved, but system complexity increases due to additional processing requirements
Solution Approach 1:
The patent segments the performance monitoring system into distinct functional modules: queue depth metric computation, correlation analysis, performance impact event detection, and alert generation. This modular approach enables automated high-efficiency detection while managing system complexity through clear separation of concerns and independent, reusable components
Solution Approach 2:
The system performs self-diagnosis by automatically computing performance metrics, analyzing correlations, and identifying performance impact events without requiring external human intervention. The automated detection system serves itself by continuously monitoring and self-evaluating system performance, reducing the need for complex external monitoring infrastructure
Data Source
AI summary
A technique manages data storage equipment. The technique involves receiving queue depth metrics from data storage performance data describing data storage performance of the data storage equipment. The technique further involves performing a performance impact detection operation on the queue depth metrics to determine whether a performance impacting event has occurred on the data storage equipment. The technique further involves, in response to a result of the performance impact detection operation indicating that a performance impacting event has occurred on the data storage equipment, launching a set of performance impact operations to address the performance impacting event that occurred on the data storage equipment. Such a technique may be performed by an electronic apparatus coupled with the data storage equipment (e.g., over a network).