Self-Learning Log Configuration Generation for Debugging
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional error logging in computing environments is inefficient, as developers often log all possible fields to debug issues, leading to performance problems, costly data storage, and difficulty in finding relevant information.
Innovation Solution
The method involves examining various input data sources to generate a log profile, which is used to create cognitive log configurations. These configurations automatically retrieve the correct attributes and fields to log before runtime, and are continuously optimized during runtime using a feedback mechanism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all possible fields are logged to ensure complete debugging information, then debugging completeness is improved, but I/O performance deteriorates and storage costs increase
Solution Approach 1:
The system extracts only the relevant log fields needed for debugging based on exception types, rather than logging all fields. The log configuration is dynamically adjusted to include only necessary attributes, reducing I/O overhead while maintaining debugging effectiveness.
Solution Approach 2:
Different log configurations are applied to different exception types or debugging scenarios. Each exception category receives a tailored log profile that includes only the specific fields relevant to that type of error, optimizing the balance between information completeness and performance.
2Reliability
If all possible fields are logged to ensure complete debugging information, then debugging completeness is improved, but storage costs increase
Solution Approach 1:
The system extracts and logs only the essential fields required for each exception type, significantly reducing the volume of log data stored. By analyzing the minimum necessary information for effective debugging, storage costs are reduced while maintaining complete debugging capability.
Solution Approach 2:
The system discards redundant or unnecessary log fields that do not contribute to debugging effectiveness. By filtering out superfluous data, storage requirements are minimized while the essential debugging information is preserved and can be recovered when needed.
3Productivity
If manual log field selection is performed to optimize performance, then I/O performance is improved, but system complexity increases
Solution Approach 1:
The system automatically generates and configures log profiles based on exception types and debugging needs, eliminating the need for manual field selection. The self-learning mechanism analyzes patterns and optimizes log configurations automatically, reducing user burden while maintaining performance optimization.
Solution Approach 2:
The system dynamically changes log configuration parameters based on the specific exception type and debugging context. Rather than requiring manual setup, the parameters are automatically adjusted to match the appropriate log profile, simplifying the user experience while optimizing performance.
4Reliability
If comprehensive log data is collected to improve debugging efficiency, then information completeness is improved, but difficulty in finding relevant information increases
Solution Approach 1:
The system extracts and presents only the relevant log fields for each exception type, filtering out unnecessary information. This extraction approach makes it easier to find and analyze the specific debugging information needed without being overwhelmed by redundant data.
Solution Approach 2:
Different log profiles provide different levels of detail appropriate to each exception type. Critical exceptions receive more detailed logging while less severe issues receive streamlined logging, making information retrieval more efficient by matching the detail level to the specific debugging need.
Data Source
AI summary
Various embodiments are provided herein for intelligent, self-learning log configuration generation for a corresponding application in a computing environment. A plurality of input data sources are examined to generate a log profile. The log profile is used to generate one or more cognitive log configurations. Those of a plurality of attributes and fields which are determined useful to be logged before a runtime operation are automatically retrieved. The plurality of attributes and fields are continuously optimized during the runtime operation using a feedback mechanism.