HTTP Transaction Root Cause Analysis Agent
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Solution Overview
Problem
Server computer systems face difficulties in tracing the root cause of performance issues due to excessive data related to performance metrics, making it challenging for users to identify the probable cause of HTTP transaction problems.
Innovation Solution
An agent with a Performance Root Cause (PRC) determiner automatically divides HTTP transaction calls into groups, identifies common parameters or attributes, and notifies users if these are present only in the group with performance issues, suggesting a probable root cause for further investigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If performance metrics data is collected comprehensively for HTTP transactions, then measurement precision is improved, but device complexity increases due to excessive data
Solution Approach 1:
The patent segments the large set of performance metrics into multiple groups based on hierarchical categories (e.g., server-level, application-level, database-level metrics). This segmentation reduces the complexity of processing all metrics simultaneously while maintaining comprehensive monitoring capability by analyzing groups independently and aggregating results.
Solution Approach 2:
The system extracts and identifies only the most relevant performance metrics that are actually causing issues, rather than processing all collected metrics. The root cause analysis functionality extracts specific problematic parameters from the vast data set, focusing computational resources on the most significant factors.
2Reliability
If all performance metrics are monitored, then reliability is improved, but ease of operation deteriorates due to difficulty in identifying root causes
Solution Approach 1:
The system implements automated feedback mechanisms that analyze performance metrics and provide actionable insights back to users. The root cause analysis functionality processes monitored data and generates feedback reports that highlight probable causes, transforming raw data into easily interpretable recommendations that maintain reliability while improving ease of operation.
Solution Approach 2:
The system performs self-service by automatically analyzing performance data and identifying root causes without requiring manual intervention. The root cause analysis engine autonomously processes metrics, correlates issues, and generates diagnostic reports, enabling the system to serve itself in terms of problem detection and initial diagnosis.
3Measurement precision
If comprehensive performance data is collected, then measurement precision is improved, but loss of time increases in analyzing the data
Solution Approach 1:
The system performs preliminary actions by pre-processing and organizing performance metrics into structured groups before actual analysis is needed. Metrics are categorized, tagged, and prepared in advance, so when performance issues occur, the analysis can quickly focus on pre-organized data rather than processing raw unstructured metrics from scratch.
Solution Approach 2:
The analysis process applies local quality by focusing computational effort on specific local areas of the data that are most likely to contain the root cause. Rather than uniformly analyzing all metrics with equal depth, the system concentrates analysis resources on the most suspicious or anomalous metric groups, reducing overall analysis time while maintaining detection accuracy.
Data Source
AI summary
The present disclosure is to determine a probable root cause of a performance issue. For example, a method can include processing, using a processor, a set of calls for a Hypertext Transfer Protocol (HTTP) transaction wherein individual calls of the set of calls have a set of parameters; and identifying, using the processor, that the HTTP transaction has a performance issue that falls below a predetermined level of a performance metric. Further steps can include separating, using the processor, the set of calls into a first group with the performance issue and a second group without the performance issue; discovering, using the processor, a common subset of parameters among the first group; and determining that the common subset of parameters is a probable root cause of the performance issue if the common subset of parameters is not found in the second group.