Distributed System Relationship Prioritization for Rapid Fault Diagnosis
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Solution Overview
Problem
In large distributed computing systems, users face challenges in identifying the critical nodes and relationships responsible for issues due to the complexity and scale of the systems, making it time-consuming to diagnose and resolve problems.
Innovation Solution
The system uses personalized, adaptive heuristics and iterative learning mechanisms to rank and highlight important relationships based on user information, employing cognitive analysis and AI to prioritize components and relationships, and dynamically alter representations based on user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If visual representations of all nodes and relationships are provided in large distributed computing systems, then complete system information is available, but it becomes challenging and time-consuming for users to identify critical nodes and relationships causing problems
Solution Approach 1:
The patent extracts and highlights only the critical relationships and nodes that are relevant to the detected impairment, rather than displaying all relationships. The system identifies impaired relationships and selectively presents them to the user, filtering out unnecessary information to enable rapid problem diagnosis while maintaining completeness of system monitoring.
Solution Approach 2:
The patent applies different visual properties to different relationships based on their relevance to the impairment. Critical relationships are highlighted with enhanced visual properties (such as color, size, or position), while non-critical relationships are dimmed or hidden. This local differentiation allows users to quickly identify problematic areas without being overwhelmed by the entire system graph.
2Reliability
If all relationships between computing nodes are displayed, then comprehensive system monitoring is achieved, but the complexity and time required to analyze the system increases
Solution Approach 1:
The system extracts only the impaired or problematic relationships from the complete set of relationships and presents them separately. By filtering out healthy, non-problematic relationships, the system maintains comprehensive monitoring capability while significantly reducing the complexity of the visual representation that users must analyze.
Solution Approach 2:
The patent segments the relationships into different categories (impaired vs. healthy, critical vs. non-critical) and presents them in a structured manner. This segmentation allows the system to maintain complete monitoring data while organizing it in a way that reduces analytical complexity and helps users focus on problematic areas.
3Loss of information
If detailed representations of all computing nodes and relationships are provided, then complete diagnostic information is available, but it is difficult to identify the cause of problems in large systems
Solution Approach 1:
The patent enhances the visual properties of relationships that are implicated in impairments, making them stand out from normal relationships. By applying different visual qualities (such as color coding, size, or positioning) to problematic relationships, the system maintains complete diagnostic information while making it significantly easier to detect and identify the cause of problems through visual inspection.
Solution Approach 2:
The system uses color changes and other visual property modifications to indicate the health status and relevance of relationships. Impaired relationships are highlighted with distinct visual properties that draw user attention, enabling rapid identification of problem causes while preserving access to complete diagnostic data about all system components.
Data Source
AI summary
Embodiments for representing the operational state of a distributed computing system are provided. Relationships within a distributed computing system are identified. Each of the identified relationships is associated with communication between two of a plurality of computing nodes within the distributed computing system. At least one difference between a healthy state of the distributed computing system and an impaired state of the distributed computing system is determined based on the identifying of the relationships. A representation of the determined at least one difference between the healthy state and the impaired state of the distributed computing system is generated based on information associated with a user.