Backend Response Time Baseline Deviation Analysis
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Solution Overview
Problem
Current application management systems lack the semantic understanding to determine the cause of unhealthy application states and differentiate between normal and abnormal data changes, leading to ineffective monitoring of backend performance.
Innovation Solution
The technology processes performance data to predict future values of backend metrics, compares them to actual values, and reports deviation information through an interface, allowing users to assess backend health by determining deviation levels and presenting this information graphically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If system management products analyze a large number of data streams to determine application state, then the coverage of monitoring is improved, but the ability to identify the ultimate source or cause of a problem deteriorates due to lack of semantic understanding
Solution Approach 1:
The patent introduces an intermediary layer (performance data analysis system) that sits between the raw data streams and the user interpretation. This intermediary performs semantic analysis by comparing actual performance data against predicted baseline values, translating raw data into meaningful deviation information that identifies the root cause of performance issues without requiring users to manually analyze numerous data streams
2Measurement precision
If system management products monitor multiple data streams without semantic understanding, then the monitoring coverage is improved, but the ability to determine whether a change indicates unhealthy state deteriorates
Solution Approach 1:
The system implements feedback by continuously comparing actual performance data against predicted baseline values and using this comparison to determine application health status. The feedback mechanism provides contextual understanding of performance changes by evaluating deviations from expected behavior, enabling reliable determination of unhealthy states rather than merely detecting changes
3Device complexity
If backend performance monitoring is implemented without baseline prediction, then the system complexity is reduced, but the ability to identify performance issues deteriorates
Solution Approach 1:
The system performs preliminary action by establishing baseline predictions of expected performance values before actual performance monitoring begins. These pre-computed baseline values serve as reference points that enable the system to identify performance issues by comparing actual measurements against predicted values, enhancing detection capability without requiring complex real-time analysis algorithms
Data Source
AI summary
Deviation of expected response times is used to characterize the health of one or more backend machines invoked by an application to process a request. Performance data generated in response to monitoring application execution is processed to select backend response time data. The selected data is processed to predict future values of a time series associated with backend response time. The predicted response time values are compared to actual response time values in the time series to determine a deviation from the predicted value. Deviation information for the time series data of response times is then reported to a user through an interface in a simple manner.