Latency Analysis Module for Transaction Performance Monitoring
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Solution Overview
Problem
Current methods for monitoring transaction or job latency in IT applications are not deterministic and are affected by noise and external influences, making it difficult to accurately assess and isolate the cause of abnormal latency.
Innovation Solution
A system comprising a data collection module and a latency analysis module that statistically characterizes normal component latencies, automatically recognizes statistically significant changes, and adapts to changes in normal behavior over time, providing a ranked list of components contributing to abnormal occurrences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional single-measure latency monitoring is used, then the monitoring method is simple, but the measurement precision is poor due to noise and external influences
Solution Approach 1:
The patent segments the overall transaction latency into multiple component latencies (e.g., database latency, application latency, network latency). By dividing the monitoring task into component-level measurements, the system achieves more precise identification of latency sources while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent introduces statistical models and algorithms as intermediaries between raw latency data and actionable insights. These intermediaries process noisy measurements, filter external influences, and transform data into deterministic assessments of component contributions, thereby improving measurement precision without proportionally increasing system complexity.
2Reliability
If component-level latency tracking is implemented, then the ability to isolate latency causes improves, but the device complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where component latency measurements continuously inform the statistical models, which in turn provide refined assessments back to the monitoring system. This closed-loop approach improves reliability by constantly adapting to new data while maintaining system complexity through automated feedback processing rather than manual analysis.
Solution Approach 2:
The patent dynamically adjusts monitoring parameters such as sampling rates, threshold values, and statistical confidence levels based on system conditions. By changing parameters adaptively rather than using fixed complex configurations, the system achieves high reliability in latency analysis while keeping the monitoring infrastructure manageable.
3Measurement precision
If statistical analysis of normal latency behavior is performed, then the detection of abnormal occurrences improves, but the loss of time for data processing increases
Solution Approach 1:
The patent performs preliminary statistical characterization of normal latency behavior during periods when the system is operating normally. By pre-computing baseline statistics (mean, standard deviation, percentiles) during normal operation, the system is prepared to quickly detect abnormalities without requiring extensive real-time processing when issues occur, thus reducing detection time while maintaining precision.
Solution Approach 2:
The patent applies statistical analysis selectively rather than continuously to all latency data. By focusing computational resources on analyzing only the components and time periods most likely to exhibit abnormalities, or by using simplified statistical models for routine monitoring and more complex analysis only when needed, the system achieves high detection precision while minimizing overall data processing time.
Data Source
AI summary
A method for analyzing a latency of a transaction performance is provided. The method includes receiving first transaction latency data which includes a transaction latency of a transaction and a first plurality of latency components that contribute to the transaction latency, receiving a definition of normality for the transaction latency, determining whether the transaction latency is normal or abnormal based at least on the definition of normality, upon the determining that the transaction latency is abnormal, determining whether there is a sufficient amount of the first transaction latency data based on a predefined criterion, upon the determining that the amount of the first transaction latency data is sufficient, computing a normal latency for each of the first plurality of latency components; and ranking the first plurality of latency components based on a degree of abnormality of each of the first plurality of latency components, which is based on the computed normal latency for the each latency component.