Dynamic Feature Flag Optimization via Machine Learning
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Solution Overview
Problem
Existing systems lack flexibility and customizability in providing application functionality, as they rely on hard-coded entitlement categories, making it difficult to optimize responsiveness and availability based on execution conditions such as user type, bandwidth, and system stress.
Innovation Solution
The use of feature flags associated with application functionality, where values determining flag activation are managed externally through a web portal and determined by machine learning at runtime, allowing for dynamic adjustment of functionality based on execution conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If functionality is hard-coded into the application for different user categories, then user entitlement management is simplified, but flexibility and customizability are reduced
Solution Approach 1:
The patent implements dynamic feature flags that can be toggled at runtime based on user entitlements and execution conditions. Instead of hard-coded static functionality, the system allows administrators to dynamically enable or disable features through a web portal, and the machine learning model dynamically determines feature flag values based on real-time conditions such as system stress and user type, thereby achieving both adaptability and reduced manual configuration complexity.
2Reliability
If all application functionality is always available, then user experience is maximized, but system responsiveness and availability deteriorate under stress
Solution Approach 1:
The patent applies partial action by selectively enabling or disabling application features based on system conditions. The machine learning model evaluates execution conditions such as system stress levels and determines which feature flags should be activated or deactivated. This allows the system to provide essential functionality while temporarily reducing non-essential features during high-stress periods, thereby maintaining system availability and responsiveness without completely sacrificing user experience.
3Ease of operation
If manual selection of functionality and conditions is performed, then control over feature execution is precise, but time and effort for configuration are increased
Solution Approach 1:
The patent implements self-service through an automated machine learning model that independently evaluates execution conditions and determines optimal feature flag values without requiring manual intervention. The system automatically monitors system stress, user type, and other execution conditions, then autonomously toggles feature flags to optimize performance. This eliminates the time-consuming manual configuration process while maintaining precise control over feature execution, as the machine learning model makes data-driven decisions based on real-time conditions.
4Adaptability or versatility
If feature values are embedded in code, then development control is maintained, but runtime flexibility and customizability are reduced
Solution Approach 1:
The patent segments the feature configuration system into distinct components: feature flags embedded in code, external web portal for value management, and machine learning model for automated determination. This segmentation allows developers to maintain control over which features exist in the codebase while enabling runtime flexibility through external value management. The system architecture becomes more complex in structure but gains significant adaptability, as administrators can manage feature values through the web portal and the machine learning model can dynamically adjust features based on execution conditions without requiring code changes.
Data Source
AI summary
A method, apparatus, and system provide the ability to optimize execution of an application. An application is acquired. The application includes functions, and each function has a corresponding feature flag that determines whether the corresponding function is executed. Execution conditions of execution of the application are monitored at run-time (in a machine learning module). The machine learning module recognizes a pattern relating to the execution conditions to determine a stress relating to the execution of the application. During execution of the application, the machine learning module toggles the feature flags based on the pattern and the stress such that the corresponding functions do not execute.