Multi-Node Diagnostic System Root Cause Analysis
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Solution Overview
Problem
Current diagnostic systems face challenges in identifying the root cause of errors across multiple nodes in computer systems, as errors on one node can be caused by issues on another node, making it difficult to pinpoint the source of the problem effectively.
Innovation Solution
A diagnostic system architecture that traverses nodes based on rules associated with each node, using metrics and error types to select nodes for analysis, and employing predictive analytics based on trends to anticipate future errors, thereby identifying root causes and preventing errors across the system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If diagnostic rules are applied across multiple nodes to identify root causes, then diagnostic accuracy is improved, but system complexity increases
Solution Approach 1:
The diagnostic system is segmented into node-specific diagnostic engines that independently evaluate local nodes, with results aggregated to identify root causes across the distributed system. This segmentation allows accurate multi-node diagnostics while managing complexity through modular, independent evaluation units.
Solution Approach 2:
A central coordination layer acts as an intermediary that manages the execution of diagnostic rules across multiple nodes, aggregates results, and synthesizes root cause analysis. This intermediary coordinates the complex interactions between nodes without requiring direct peer-to-peer complexity management.
2Reliability
If comprehensive node analysis is performed to identify root causes, then error detection capability is improved, but analysis time increases
Solution Approach 1:
Diagnostic rules are pre-configured and prepared for each node type, with common error patterns and analysis procedures established in advance. This preliminary preparation enables rapid execution of comprehensive node analysis without ad-hoc decision-making during actual diagnostics.
Solution Approach 2:
The diagnostic system dynamically adapts the scope and depth of analysis based on initial error symptoms and node interdependencies. High-risk or frequently failing nodes receive more intensive analysis, while stable nodes undergo lighter evaluation, optimizing the balance between detection capability and analysis time.
Data Source
AI summary
Techniques for running diagnostic rules across multiple nodes are disclosed. A root cause of an error occurring on a particular node is determined. A root cause of an error on the particular node is identified based on a metric corresponding to another node and/or based on a previously-reported error on another node. Additionally or alternatively, an error is predicted to occur on a particular node. The error is predicted based on a trend associated with a metric corresponding to another node.