Automated Tracing System with Real-Time Function Analysis
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Solution Overview
Problem
Existing application tracing systems face inefficiencies in resource utilization and data management, as they often trace all functions equally, leading to excessive overhead and unnecessary data collection, which can slow applications and consume vast resources.
Innovation Solution
An automated tracing system that creates wrapping functions for each application function, including callback functions, to selectively trace only significant functions based on real-time analysis of tracing data, using evaluation mechanisms like whitelists, blacklists, and user interactions to adjust tracing configurations dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all functions are traced equally, then complete monitoring coverage is achieved, but tracing overhead and resource consumption increase significantly
Solution Approach 1:
The patent applies different tracing configurations to different functions based on their significance. The system evaluates functions using whitelists, blacklists, and expressions to identify which functions require detailed tracing and which can be traced minimally or excluded. This creates local quality variations in tracing depth across different parts of the application, reducing overall overhead while maintaining coverage of critical functions.
Solution Approach 2:
The tracing system dynamically adjusts its behavior based on real-time analysis of tracing data. The evaluation mechanism continuously assesses function significance and modifies tracing configurations accordingly, allowing the system to adapt to changing conditions and optimize the balance between monitoring coverage and resource consumption during application execution.
2Loss of information
If tracing data is collected for all functions, then comprehensive performance data is obtained, but data management complexity and storage requirements increase
Solution Approach 1:
The patent extracts and focuses tracing efforts on only the most significant functions rather than collecting data from all functions. By using evaluation mechanisms to identify key functions, the system extracts the essential performance information needed while discarding redundant data from less important functions, thereby reducing data management complexity while maintaining data completeness for critical areas.
Solution Approach 2:
The tracing system segments functions into different categories based on their significance to application performance. Using whitelists, blacklists, and evaluation expressions, the system divides functions into groups that require different levels of tracing, allowing selective data collection and simplified management of traced data while ensuring comprehensive monitoring of critical segments.
3Measurement precision
If detailed tracing is applied to all functions, then precise performance measurement is achieved, but application execution speed decreases
Solution Approach 1:
The patent implements local quality by applying detailed tracing only to specific significant functions identified through the evaluation mechanism, while using lighter or no tracing for other functions. This localized approach to measurement precision maintains accurate performance data for critical functions without imposing the overhead of detailed tracing across the entire application, thereby preserving execution speed.
Solution Approach 2:
The system applies partial action by collecting detailed tracing data only for the portion of functions that are most significant to performance, rather than applying excessive tracing to all functions. The evaluation mechanism identifies which functions warrant detailed measurement, allowing the system to achieve sufficient measurement precision for performance analysis without the speed penalty of comprehensive detailed tracing.
Data Source
AI summary
Real time analysis of tracing data may identify functions for which tracing may be enhanced or reduced. A tracer that generates function-level data may have an aggregator that summarizes the data. Potential changes to tracing configuration may be identified by analyzing the summarized data to determine whether or not each function is being traced at a level commensurate with that function's impact to the summarized data. Those functions with little significant contribution may have their tracing reduced, while those functions with more significant contribution may have their tracing enhanced. The analysis of the summarized data may be performed in real time in some instances, causing a tracer to change the data collected while an application executes.