Application Configuration Selection via Machine Learning
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Solution Overview
Problem
Users face challenges in configuring applications for computing environments, such as cloud computing, where varying conditions require different settings, leading to inefficient resource utilization and potential misconfiguration, especially during migrations between environments.
Innovation Solution
A method involving a machine learning model that identifies changes to configuration settings by executing an application under initial and updated configurations, measuring metrics, and selecting the optimal configuration for future use based on performance and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual configuration updates are performed when migrating applications between computing environments, then configuration accuracy may be improved, but time consumption and operational complexity increase significantly
Solution Approach 1:
The system performs preliminary actions by automatically detecting the source computing environment configuration and pre-generating the target configuration before migration occurs. The configuration translation and validation are completed in advance, so that when migration happens, the configuration is already optimized and ready, eliminating the need for manual post-migration adjustments.
Solution Approach 2:
The configuration management system serves itself by automatically translating configurations between different computing environments without human intervention. The system self-adapts to different environment requirements, self-validates configuration compatibility, and self-corrects potential issues, freeing operators from manual configuration tasks.
2Productivity
If multiple configuration settings are tested to find the optimal configuration, then application performance can be improved, but the complexity of the configuration process increases
Solution Approach 1:
The configuration system is made dynamic by implementing automated translation and adaptation mechanisms that adjust configurations in real-time based on the target computing environment characteristics. Rather than static manual configuration, the system dynamically generates and validates configurations, making the process flexible and adaptive without requiring complex manual intervention.
Solution Approach 2:
An intermediary configuration translation service is introduced between the source and target environments. This intermediary automatically translates configurations from one environment format to another, validates compatibility, and resolves conflicts, simplifying the overall process by centralizing the complexity in an automated service rather than requiring complex manual procedures.
3Reliability
If configuration validation is performed thoroughly before deployment, then reliability of application execution is improved, but the time required for deployment increases
Solution Approach 1:
Configuration validation is performed as a preliminary action during the automatic translation process, before deployment occurs. The system validates configuration syntax, semantics, and compatibility with the target environment in advance, ensuring that only validated configurations are deployed. This preliminary validation eliminates the need for post-deployment validation and reduces deployment time.
Solution Approach 2:
The validation process is integrated continuously into the configuration translation and generation workflow rather than being a separate batch process. As the system translates and generates configurations, validation occurs continuously in the background, ensuring that the useful action of configuration preparation continues without interruption while maintaining high reliability standards.
Data Source
AI summary
Methods and systems for selecting, testing, and applying application configurations are presented. In one embodiment, a method is provided that includes executing an application according to a first configuration and measuring a first plurality of metrics. One or more changes to a plurality of configuration settings of the first configuration may be identified by a machine learning model to generate one or more new configurations. Among the one or more new configurations, a second configuration for future executions of the application may be selected based on the first plurality of metrics and a second plurality of metrics associated with an execution of the application.