Dynamic Log Level Management in Service Meshes
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current application management systems require static log generation settings, which can lead to insufficient detail for troubleshooting and analysis, causing difficulties in monitoring and robotic process automation, and may result in performance slowdowns due to high logging levels.
Innovation Solution
A service mesh leveraging AI systems to dynamically adjust log levels based on historical metrics and error predictions, increasing detail when errors are predicted and reducing it when stability is confirmed, to balance logging with performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If static log generation settings are used, then system performance is maintained, but insufficient detail is captured for troubleshooting and analysis
Solution Approach 1:
The patent implements dynamic log level adjustment where the service mesh automatically changes log levels from static to dynamic based on real-time system state. The control plane receives system metrics, determines appropriate log levels, and configures data plane components accordingly, allowing log detail to adapt to current operational needs while maintaining performance.
Solution Approach 2:
The system changes the log level parameter dynamically based on system conditions. The control plane adjusts the log level parameter (e.g., from INFO to DEBUG or vice versa) according to received system metrics and determined log policies, enabling the system to capture appropriate detail without consistently overhead-ing performance.
2Reliability
If high logging levels are used, then sufficient information is captured for diagnosis, but performance overhead increases
Solution Approach 1:
The patent applies different log levels to different components or contexts locally rather than uniformly across the entire system. The control plane can determine specific log levels for specific data plane components based on their individual system metrics and importance, allowing detailed logging only where necessary for error prediction while minimizing overall performance overhead.
Solution Approach 2:
The system applies logging at higher levels partially rather than excessively across all components. By selectively increasing log detail only for specific components or time periods when needed for error prediction, the system achieves sufficient diagnostic information without the full performance cost of high-level logging everywhere.
3Productivity
If dynamic log level adjustment is implemented, then logging efficiency is improved, but system complexity increases
Solution Approach 1:
The patent segments the logging control function into separate components: the control plane handles log level determination and policy management, while the data plane handles actual log generation. This segmentation allows dynamic log level adjustment without requiring every component to implement complex decision logic, improving logging efficiency while managing system complexity through clear separation of concerns.
Solution Approach 2:
The control plane acts as an intermediary between system metrics and log level configuration. Rather than having data plane components directly analyze metrics and adjust their own logging (which would increase their complexity), the control plane receives metrics, determines appropriate log levels, and configures data plane components, simplifying the architecture while enabling dynamic adjustment.
Data Source
AI summary
Systems, methods and/or computer program products dynamically managing log levels of microservices in a service mesh based on predicted error rates of calls made to the service mesh. A first AI module predicts health, status and/or failures of microservices individually or as part of microservice chains with a particular confidence level. Using health status mapped to the microservices and historical information inputted into a knowledge base (including error rates), the first AI module predicts error rates of the API call for each user profile or generally by the service mesh. A second AI module analyzes the predictions provided by the first AI module and determines whether the predictions meet threshold levels of confidence. To improve the confidence of predictions that are below threshold levels, the second AI module dynamically adjusts application logs of the microservices and/or proxies thereof to an appropriate level to capture more detailed information within the logs.