Mini-Chart Topology Map for I/O Path Bottleneck Detection
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Solution Overview
Problem
Current performance data collection techniques in storage area networks are complex and prone to user error, especially when I/O data paths change, as they require manual configuration updates, which can be time-consuming and inefficient.
Innovation Solution
A system that generates and displays mini-charts on a topology map, allowing for dynamic criticality determination and visual designation based on performance data thresholds, enabling automatic selection and display of full data charts, and includes a performance data collection tool that automatically adjusts data collection settings in response to changes in I/O data paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual configuration updates are used to track I/O data path changes, then performance data collection can be maintained, but the system becomes complex and prone to user error
Solution Approach 1:
The system automatically discovers I/O data paths and updates performance data collection configurations without requiring manual user intervention. The discovery mechanism autonomously identifies changes in I/O paths and adjusts the monitoring setup accordingly, eliminating the need for users to manually track and update configurations.
Solution Approach 2:
The system implements a feedback loop where performance data collection is continuously monitored and automatically adjusted based on discovered I/O data path changes. The system detects changes in the I/O landscape and feeds this information back into the configuration management, automatically updating the performance monitoring setup to reflect current system state.
2Reliability
If manual configuration updates are required when I/O data paths change, then performance data can be collected, but time is lost and efficiency is reduced
Solution Approach 1:
The system performs preliminary discovery of I/O data paths and proactively prepares performance data collection configurations before changes occur. By continuously monitoring the I/O landscape and anticipating changes, the system automatically updates configurations in advance, eliminating the need for reactive manual updates and reducing downtime.
Solution Approach 2:
The configuration update process is automated through self-service mechanisms that detect I/O data path changes and independently adjust performance monitoring settings without requiring user intervention. This automation eliminates the time-consuming manual configuration update process while maintaining data collection reliability.
3Loss of information
If comprehensive performance data is collected for all objects, then complete monitoring is achieved, but the visualization becomes complex and difficult to interpret
Solution Approach 1:
The system segments performance data visualization by organizing it along I/O data paths rather than presenting all data in a single complex view. Each I/O path is displayed as a separate, manageable unit showing only the relevant performance metrics for that specific path, making the data easier to interpret while maintaining completeness through systematic organization.
Solution Approach 2:
The visualization applies local quality by tailoring the display of performance data to the specific context of each I/O path. Each segment of the visualization presents information optimized for that particular path's characteristics and relevance, rather than applying a uniform presentation to all data, thereby improving interpretability without losing information.
Data Source
AI summary
A system provides for display of results from performance data analysis that presents mini-chart overlays on a topology map based on intelligent analytics for components in a I/O data path of a host or other object. The mini-charts may be advantageously used to show criticality of impact to assist a user with incident avoidance and/or to provide fast incident resolution. When launching into the topology map, the user may enable mini-charts and visually see from the charts where a possible performance bottleneck is detected by the analytics. In an embodiment, the mini-charts for the most relevant metrics may be displayed and identified for criticality, such as by color. The colors may be determined by thresholds, that may be set by a user and/or determined by the system, and may be calculated automatically based on learned base line, maximum line and minimum line operations over set times.