Modular Diagnostic System for Root Cause Analysis
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Solution Overview
Problem
Complex computing devices with numerous processes and threads performing similar operations make it difficult to detect the cause of performance issues, leading to poor device operation and user dissatisfaction.
Innovation Solution
A modularized collaborative performance issue diagnostic system that monitors events, identifies causality chains, and determines the root cause of performance issues through multiple issue detectors, analysis core modules, and scenario modules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple processes and threads are running simultaneously in computing devices, then computing device functionality and operation capability are improved, but difficulty of detecting and measuring performance issues worsens
Solution Approach 1:
The system segments the complex monitoring task by dividing it into multiple specialized issue detectors, each responsible for detecting specific types of performance issues in different system layers (application layer, system layer, hardware layer). This segmentation allows the system to handle the complexity of multiple processes and threads by breaking down the detection problem into manageable, specialized components rather than attempting to monitor all processes simultaneously with a single mechanism.
2Productivity
If the number of processes and threads increases, then computing device functionality is improved, but device complexity increases
Solution Approach 1:
The system is divided into distinct modular components including issue detectors, causality chain builders, and root cause analyzers. Each component performs a specific function in the performance issue detection pipeline, allowing the system to manage complexity through functional segmentation. This modular architecture enables the system to process information from numerous processes and threads without becoming unwieldy, as each module handles a specific aspect of the analysis.
Solution Approach 2:
The causality chain builder acts as an intermediary component that connects issue detection with root cause analysis. It receives data from multiple issue detectors and systematically constructs causality chains that link observed performance issues to their underlying causes. This intermediary layer simplifies the overall system complexity by providing a structured method for connecting disparate data points without requiring direct complex interactions between all system components.
3Ease of manufacture
If traditional performance monitoring methods are used, then implementation simplicity is maintained, but measurement precision of performance issue causes deteriorates
Solution Approach 1:
The system replaces traditional mechanical or manual performance monitoring approaches with an automated electronic system that uses issue detectors, event logs, and algorithmic causality chain construction. This substitution maintains ease of implementation through automation while dramatically improving measurement precision by systematically analyzing system events and constructing detailed causality chains that precisely identify root causes of performance issues.
Solution Approach 2:
The system implements feedback mechanisms where issue detectors continuously monitor system performance, detect anomalies, and trigger detailed analysis through the causality chain builder. This feedback loop ensures high measurement precision by systematically investigating performance issues as they occur, rather than relying on periodic or manual checks. The feedback mechanism maintains implementation simplicity by automating the entire detection and analysis process.
Data Source
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AI summary
A collaborative diagnostic system monitors events in a system and identifies a causality chain from a detected performance issue to the root cause of that performance issue. The collaborative diagnostic system includes multiple issue detectors, multiple analysis core modules, and multiple scenario modules that work together to identify the causality chain and root cause. Each issue detector is a module or component that includes logic to detect known behaviors in the system, such as performance issues in the system. Each analysis core module includes logic to analyze and correlate low level system behavior within a conceptual area. Within each analysis core module are one or more diagnostic modules that are specific to that analysis core module to help determine what is happening in the system. Each scenario module includes logic to take an appropriate responsive action in response to the root cause of a performance issue being determined.