Datacenter Health Visualization via Scatterplot
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Solution Overview
Problem
As datacenter virtual infrastructure grows, existing monitoring solutions become unscalable and difficult to interpret, making it challenging to monitor and troubleshoot health issues across numerous objects effectively.
Innovation Solution
A dashboard with a scatterplot visualization that displays datacenter objects based on problem severity and time, allowing for prioritization, monitoring, and troubleshooting by mapping object importance, problem severity, and duration, along with interactive features like time sliders and directed edges to show relationships among objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If existing monitoring solutions use lengthy lists of Top items to display datacenter health, then comprehensive monitoring coverage is achieved, but user interpretability and quick understanding deteriorate
Solution Approach 1:
The patent transforms the traditional one-dimensional flat list of monitoring items into a two-dimensional scatter plot visualization. Each datacenter object is represented as a point positioned according to its problem severity (y-axis) and time characteristics (x-axis), enabling users to quickly comprehend the health status of numerous objects simultaneously without having to scan through lengthy lists.
2Quantity of substance
If monitoring solutions display many lists of items, then comprehensive monitoring is achieved, but scalability deteriorates due to reading and scrolling requirements
Solution Approach 1:
By organizing monitoring data in a scatter plot with problem severity on the y-axis and time on the x-axis, the system allows users to visually assess the status of all monitored objects at once. This eliminates the need to read and scroll through multiple lists, significantly improving user efficiency while maintaining comprehensive monitoring of numerous datacenter objects.
3Quantity of substance
If traditional dashboards display flat lists of items, then all items can be listed, but prioritization of critical problems deteriorates
Solution Approach 1:
The scatter plot applies local quality by differentiating the visual representation of objects based on their problem severity. Objects with higher problem severity are positioned higher on the y-axis, making them naturally stand out to users. This spatial differentiation based on local characteristics enables automatic prioritization of critical problems without requiring users to scan through lists to identify what needs attention.
Data Source
AI summary
Embodiments of the present invention provide a dashboard that displays an overview of a datacenter's health which helps prioritize, monitor, and troubleshoot problems. In particular, one embodiment is a method for visualizing the health of datacenter objects which includes displaying datacenter objects on a scatterplot of a dashboard wherein one axis of the scatterplot corresponds to problem severity and another axis of the scatterplot corresponds to time.