Application Performance Dashboard Using ML Pattern Recognition
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Solution Overview
Problem
Current application performance management systems rely on static, inflexible rule-based alerting, which often provides inaccurate information for dynamic data, making it difficult to obtain accurate and meaningful insights into continuously running software systems, and results in inaccurate reporting.
Innovation Solution
The system monitors applications using short-term and long-term data analysis, including machine learning-based pattern recognition, to generate weighted transaction metric scores, which are aggregated into a scaled application performance score, and reported through a dynamically updated dashboard, providing graphical health insights over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rule-based alerting is used for application performance monitoring, then the system provides continuous monitoring capability, but the accuracy of information provided deteriorates due to static and inflexible nature
Solution Approach 1:
The system transitions from static rule-based alerting to dynamic machine learning models that continuously adapt to changing application behavior patterns. The ML models are trained on historical data and update their parameters over time to accurately reflect current system states, enabling both continuous monitoring and high measurement precision.
Solution Approach 2:
The system changes the fundamental parameters of analysis by moving from fixed threshold rules to probabilistic machine learning models with multiple variables. These models analyze patterns across numerous parameters simultaneously (response time, error rates, transaction volumes) and dynamically adjust their interpretation based on learned relationships, improving accuracy while maintaining continuous monitoring.
2Measurement precision
If multiple analysis techniques are used to improve accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The system segments the analysis into distinct functional modules: data collection agents, short-term ML analysis components, long-term trend analysis components, and dashboard presentation layers. Each module has a specific responsibility and can be independently configured, trained, and maintained, reducing overall system complexity while enabling multiple analysis techniques to work together.
Solution Approach 2:
The system introduces intermediary components that bridge different analysis techniques. Normalization layers standardize data from various sources before analysis, aggregation layers combine results from multiple ML models, and interpretation layers translate complex model outputs into actionable insights. These intermediaries manage complexity by providing standardized interfaces between components.
Data Source
AI summary
A system monitors applications, analyzes metrics, and provides a dashboard that communicates whether an application is performing as expected. The metric analysis includes performing one or more of a first short term data analysis, a second short term analysis based on machine learning-based pattern recognition machines, and a long-term analysis is performed. Transaction performance metrices are determined based on the monitored of the application. The transaction performance metrices are scored, scaled, and aggregated into a single scaled representation for the application. The scaled application value is then reported to a user through a dynamically updated dashboard. The dashboard displays graphical information representing the health of monitored transactions over time. The reported information can be expanded to additional layers of detail.