Adaptive Logging Engine for Dynamic Resource Optimization
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Solution Overview
Problem
Computing systems face challenges in balancing the need for detailed logs for error diagnosis with the resource and space constraints, as extensive logging consumes significant system resources and storage.
Innovation Solution
Implementing a data protection manager that intelligently enables enhanced logging by predicting when and at what level to increase logging based on error likelihood, using a logging engine to analyze historical data and telemetry information to determine if enhanced logging is necessary, and dynamically adjusting logging levels to minimize resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If enhanced logging is continuously enabled to capture detailed system behaviors for error diagnosis, then measurement precision and reliability are improved, but use of energy and storage space deteriorate due to significant resource consumption
Solution Approach 1:
The patent implements dynamic logging level adjustment where the system transitions from static continuous enhanced logging to dynamic adaptive logging. The logging level is adjusted in real-time based on system state predictions, error likelihood assessments, and actual error occurrences, allowing the system to optimize between detailed logging and resource conservation
Solution Approach 2:
The system changes the logging parameter (logging level) based on system conditions. By using machine learning models to predict error likelihood and system state, the logging level parameter is adjusted between different states (enhanced, normal, minimal), thereby changing the amount of resources consumed while maintaining diagnostic capability when needed
2Measurement precision
If enhanced logging is continuously enabled to capture detailed system behaviors for error diagnosis, then measurement precision is improved, but storage space deteriorates due to significant space consumption
Solution Approach 1:
The patent implements dynamic logging level adjustment where the system transitions from static continuous enhanced logging to dynamic adaptive logging. The logging level is adjusted in real-time based on system state predictions, error likelihood assessments, and actual error occurrences, allowing the system to optimize between detailed logging and storage conservation
Solution Approach 2:
The system changes the logging parameter (logging level) based on system conditions. By using machine learning models to predict error likelihood and system state, the logging level parameter is adjusted between different states (enhanced, normal, minimal), thereby changing the amount of storage space consumed while maintaining diagnostic capability when needed
3Loss of energy
If logging level is dynamically adjusted based on error likelihood prediction to conserve resources, then loss of energy and storage are reduced, but measurement precision deteriorates during normal operation due to reduced logging
Solution Approach 1:
The system performs preliminary actions by using machine learning models to predict future error likelihood and system states before actual errors occur. Based on these predictions, the logging level is proactively adjusted to enhanced mode in advance of potential errors, ensuring detailed logging is ready when needed while maintaining resource efficiency during normal operation
Solution Approach 2:
The system implements feedback loops where actual error occurrences and system behaviors are continuously monitored and fed back to the machine learning models. This feedback refines the prediction accuracy and allows the system to learn from past errors, improving its ability to anticipate when enhanced logging should be activated, thereby balancing resource usage with measurement precision
Data Source
AI summary
In general, in one aspect, the invention relates to a method for enabling enhanced logging. The method includes obtaining a log associated with a job; determining, using the log, that enhanced logging is to be enabled prior to initiating the job; enabling, in response to the determination, enhanced logging on at least one node, and initiating servicing of the job, after the enabling, on the at least one node.