Visual Analytics for Network Fault Diagnosis
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Solution Overview
Problem
Network diagnosis in complex modern networks is challenging due to the difficulty in accurately identifying the root cause of faults, as automated tools often provide incorrect diagnoses, requiring system administrators to manually verify and analyze large amounts of data across multiple levels of detail.
Innovation Solution
A visual analytics system is coupled with an automated diagnostic system, providing an interactive user interface that allows seamless navigation across different levels of detail, including variable, component, edge, and network levels, using directed graphs and a multi-level analytic reasoning engine for effective exploration and verification of diagnostic results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated diagnostic tools are used to diagnose network faults, then diagnostic speed is improved, but diagnostic accuracy deteriorates due to incorrect diagnoses requiring manual verification
Solution Approach 1:
The patent introduces a visual analytics system as an intermediary between automated diagnostic tools and system administrators. This intermediary provides graphical visualizations of network components, performance metrics, and diagnostic results, allowing administrators to quickly verify automated diagnoses without manually analyzing raw data. The visual interface acts as a mediator that enhances both the speed and accuracy of fault diagnosis by making automated tool outputs more interpretable and verifiable.
2Measurement precision
If system administrators manually verify automated diagnostic results, then diagnostic accuracy is improved, but time consumption increases due to large amounts of data to be analyzed
Solution Approach 1:
The patent creates visual copies and representations of complex network data and diagnostic results. Instead of requiring administrators to analyze raw diagnostic data directly, the system generates graphical copies including network topology visualizations, performance metric charts, and component status representations. These visual copies enable quick verification of automated diagnoses without the time-consuming process of manually interpreting raw data, thus maintaining diagnostic accuracy while reducing time consumption.
3Measurement precision
If detailed network data is displayed for manual analysis, then diagnostic accuracy is improved, but system complexity increases making the interface difficult to use
Solution Approach 1:
The patent segments the display of network diagnostic information into multiple organized views and layers. The visual analytics system divides complex network data into manageable components such as network topology views, component detail views, performance metric views, and diagnostic result views. Administrators can navigate between these segmented views and drill down from high-level summaries to detailed information as needed. This segmentation maintains diagnostic accuracy by providing access to detailed data while improving interface usability by organizing information in structured, navigable sections rather than presenting all data at once.
Data Source
AI summary
Described is a visual analytics system for network diagnostics. The visual analytics system obtains network diagnostic-related information from a diagnostic system. The visual analytics system includes an interactive user interface that displays the representations of network components, including network machines and, zero or more links between those components, (e.g., as appropriate based upon selection or dynamic conditions). The user interface includes a main network view that displays representations of network components, a diagnostics view that displays suggested diagnosis results obtained from the diagnostic system, and a performance counter view that displays performance counter data. User interaction with one of the views correspondingly changes the displays in the other views. The system allows effective exploration of multiple levels of detail, e.g., variable, component, edge level and network levels, for example, via flexible navigation across these levels from the top, the bottom, or anywhere in the middle, while retaining context.