Probabilistic Model Causal Graphs Computing Performance
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Solution Overview
Problem
Computing environments often face performance issues due to resource shortages and degradation, which are difficult to predict and prevent using existing technologies, leading to costly reactive measures and potential system interruptions.
Innovation Solution
A digital processing system forms a causal dependency graph to identify usage dependencies among components, trains a probabilistic model to correlate performance indicators with past incidents, and uses a fuzzy matching algorithm to detect imminent performance issues, enabling proactive resource allocation and preventive actions to avoid these issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional monitoring and reactive measures are used, then system response time is reduced, but system reliability deteriorates due to performance issues occurring before detection
Solution Approach 1:
The system performs preliminary actions by training a probabilistic model with historical incident data and forming causal dependency graphs in advance. This enables the system to predict and proactively address performance issues before they occur, improving reliability without sacrificing response time when actual issues arise.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring performance indicators, comparing them against the trained probabilistic model, and using the results to update predictions and trigger preventive actions. This closed-loop feedback enables the system to maintain high reliability while responding efficiently to actual performance degradation.
2Measurement precision
If complex probabilistic models and causal graphs are implemented, then measurement precision of performance issues improves, but device complexity increases
Solution Approach 1:
The system segments the complex analysis task into distinct components: forming causal dependency graphs that map component relationships, training probabilistic models with historical data, and using fuzzy matching algorithms for pattern recognition. This segmentation makes the complex system more manageable and implementable while maintaining high detection precision.
3Productivity
If proactive prediction and preventive actions are implemented, then system productivity is improved by avoiding performance issues, but loss of time increases due to data collection and analysis
Solution Approach 1:
The system performs data collection, causal graph formation, and model training as preliminary actions during periods when the system is operating normally. This advance preparation enables rapid prediction and response when performance issues arise, improving overall productivity without significant impact on operational response time.
Data Source
AI summary
Proactive avoidance of performance issues in computing environments. In one embodiment, a causal dependency graph representing the usage dependencies among the various components of a computing environment is formed, the components being associated with key performance indicators (KPIs). A probabilistic model is trained with prior incidents that have occurred in the components to correlate outliers of KPIs in associated components to prior incidents. The training includes determining the correlation based on the causal dependency graph. Upon detecting the occurrence of outliers for performance metrics, an imminent performance issue likely to occur in a specific component is identified based on the probabilistic model and the detected outliers. A preventive action is performed to avoid the occurrence of the imminent performance issue in the specific component.