3D Grid Visualization for Computing System Fault Diagnosis
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Solution Overview
Problem
Current network and systems management tools face challenges in efficiently diagnosing and addressing faults in complex computing systems due to the sheer volume of event data, evolving network configurations, and the need for detailed knowledge of underlying relationships between devices, leading to increased time and cost for service engineers.
Innovation Solution
A system and method that utilizes graphical techniques and computational geometry to summarize event data into meaningful n-dimensional event vectors, allowing network administrators to visualize and analyze device and resource status relationships without requiring explicit knowledge of underlying configurations, using vector formatting and similarity calculations to identify root causes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional table-based event data representation is used, then all event information is preserved, but service engineers spend excessive time analyzing large volumes of data to identify root causes
Solution Approach 1:
The patent extracts only the most relevant event information by creating condensed visual summaries that highlight key patterns and relationships. Instead of displaying all raw event data in tables, the system extracts essential diagnostic information and presents it in focused visual representations, allowing engineers to quickly identify root causes without being overwhelmed by data volume.
Solution Approach 2:
The patent transforms one-dimensional tabular data into multi-dimensional visual representations. Event data is organized into hierarchical views that display relationships across multiple dimensions (time, device type, event category, severity), enabling engineers to perceive patterns and correlations that are invisible in traditional flat tables.
2Measurement precision
If detailed knowledge of network configurations and device relationships is required for diagnosis, then accurate root cause identification is possible, but the complexity of the diagnostic process increases significantly
Solution Approach 1:
The patent implements self-service diagnostics by automatically analyzing event data and generating visual summaries that highlight potential root causes. The system performs self-diagnosis through automated pattern recognition and correlation analysis, reducing the need for engineers to manually navigate complex configuration details while maintaining high diagnostic accuracy.
Solution Approach 2:
The patent introduces visual event summaries as an intermediary between raw event data and engineer analysis. These summaries act as a mediator that pre-processes and organizes complex configuration and event relationship data, presenting distilled insights that maintain diagnostic accuracy while simplifying the engineer's task.
3Reliability
If comprehensive event monitoring is implemented across all devices, then complete fault coverage is achieved, but the volume of data to be analyzed increases exponentially
Solution Approach 1:
The patent segments event data by device type, event category, and severity level, organizing comprehensive monitoring data into manageable segments. Each segment is visually summarized with relevant metrics and patterns, allowing the system to maintain complete fault coverage while presenting data in digestible portions that reduce analysis complexity.
Solution Approach 2:
The patent applies partial action by focusing visual attention on the most significant events and patterns rather than displaying all events equally. The system uses filtering and prioritization to show only the events and relationships that are most relevant to current diagnostic needs, effectively managing data volume while maintaining comprehensive monitoring capability.
Data Source
AI summary
Disclosed herein is a data visualization methodology to assist network administrators and service engineers for complex computing systems in diagnosing and addressing faults, errors and other conditions within the computing system, and related computerized processes and network architectures and systems supporting the methodology. The methodology produces a visual representation of the relative severity and similarity of a various fault conditions occurring within a plurality of devices within the complex computing system. The visual representation visually depicts the various computing devices and/or resources within a three-dimensional grid located according to a first axis representing the severity of fault conditions being experienced by the devices/resources and second and third axes representing the similarity/disparity of the types of fault conditions being experienced by the devices/resources.