Dynamic Application Logging Mechanism for Intermittent Error Diagnosis
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Solution Overview
Problem
Existing application logging mechanisms do not provide sufficient information to diagnose intermittent errors effectively, leading to time-consuming processes to reproduce issues and excessive irrelevant data generation when log levels are set high.
Innovation Solution
A dynamic application logging mechanism that activates logging at the highest level just prior to a fault and deactivates it after a predetermined time or when the fault is addressed, using predictive modeling to adjust log levels based on system states and failure probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If log levels are set to the highest level at all times, then sufficient information is available to diagnose errors, but excessive irrelevant data is generated consuming storage space and processing resources
Solution Approach 1:
The logging system dynamically adjusts the logging level based on the current system state and predicted failure probability. Instead of using a static high logging level, the system transitions between different logging levels (e.g., from normal to detailed) according to real-time conditions, thereby generating sufficient diagnostic information only when necessary while minimizing irrelevant data during normal operation.
Solution Approach 2:
The system changes the logging parameter (log level) based on the predicted probability of failure. When the probability exceeds a threshold, the logging level is increased to capture detailed information; when the probability is low, the logging level is reduced to minimize data volume. This parameter adjustment resolves the contradiction between data completeness and data volume.
2Measurement precision
If log levels are increased to improve error diagnosis, then more diagnostic information is captured, but application performance deteriorates due to processing overhead
Solution Approach 1:
The logging mechanism dynamically adjusts its operation mode based on the predicted failure probability. During normal operation with low failure probability, detailed logging is disabled to maintain application performance. When the probability of failure increases, the system activates detailed logging to capture diagnostic information, thus balancing performance and diagnostic capability through dynamic adaptation.
Solution Approach 2:
The system performs preliminary prediction of failure probability using machine learning models before actual failures occur. Based on this preliminary assessment, the logging level is proactively adjusted to ensure diagnostic information is available when needed, rather than relying on post-failure analysis of insufficient data. This prevents performance degradation while maintaining diagnostic readiness.
3Loss of information
If detailed logging is enabled continuously to capture intermittent errors, then diagnostic information is available, but storage space is excessively consumed
Solution Approach 1:
The system changes the logging parameter (detail level) based on the predicted probability of failure and the observed system state. When the probability of intermittent errors is high, detailed logging is enabled to capture diagnostic information. When the probability is low, logging is reduced to a minimal level, thereby preventing excessive storage consumption while ensuring error information is captured when likely to occur.
Solution Approach 2:
The logging system uses machine learning models to autonomously determine when detailed logging is necessary, eliminating the need for continuous manual monitoring or fixed high-level logging. The system serves itself by automatically adjusting logging behavior based on patterns learned from historical data, thereby optimizing the balance between information capture and storage efficiency.
Data Source
AI summary
A system to facilitate application logging is described. The system includes a processor and a machine readable medium storing instructions that, when executed, cause the processor to record a system state, perform application logging at a first logging rate, record an occurrence of task failures during the logging, determine a predicted queue size threshold value based on the recorded occurrence of task failures, determine whether that the predicted queue size threshold value is less than an actual queue size and perform the application logging at a second logging rate upon a determination that the predicted queue size threshold value is less than an actual queue size, wherein the second logging rate is greater than the first logging rate.