Reinforcement Learning for Dynamic Performance Benchmarking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Static performance benchmarking tools are ineffective due to continuous changes and updates in software applications, making it difficult to identify and address performance degradation issues.
Innovation Solution
A dynamic parametric modeling system using reinforcement learning, which retrieves and implements distributed impact simulation models, initiates reinforcement learning algorithms, and generates optimization policies to maximize aggregated rewards, thereby adapting to changes in application parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static performance benchmarking tools are used to analyze application parameters, then the analysis process is simple and straightforward, but the tools become ineffective due to continuous changes and updates in software applications
Solution Approach 1:
The patent implements dynamic parametric modeling that continuously adapts to changing application states. The system transitions from static benchmarking to dynamic modeling where parameters are continuously updated based on reinforcement learning, allowing the system to remain effective despite application changes and updates.
Solution Approach 2:
The system incorporates reinforcement learning with feedback loops that continuously monitor application performance and update the parametric model accordingly. The feedback mechanism allows the system to learn from application behavior changes and adapt the benchmarking parameters dynamically, resolving the contradiction between simplicity and adaptability.
2Reliability
If reinforcement learning algorithms are implemented to dynamically model application parameters, then the system adaptability improves, but the computational complexity and processing requirements increase
Solution Approach 1:
The patent segments the benchmarking process into distinct phases: initial static analysis, reinforcement learning-based dynamic modeling, and optimization policy generation. This segmentation allows the system to apply computational intensity only where necessary (in the dynamic modeling phase) while maintaining simplicity in other phases, thereby improving reliability without overwhelming computational complexity.
Solution Approach 2:
The system performs preliminary static analysis before applying reinforcement learning, establishing a baseline model that captures initial application characteristics. This preliminary action reduces the computational burden of subsequent reinforcement learning by providing a starting point that requires less intensive processing to refine.
3Measurement precision
If distributed impact simulation models are used to test application parameters, then the measurement precision improves, but the time required for testing and analysis increases
Solution Approach 1:
The patent implements periodic action by executing distributed impact simulation models at strategically determined intervals rather than continuously. The reinforcement learning algorithm identifies when re-testing is necessary based on application state changes, allowing the system to maintain high measurement precision while minimizing unnecessary testing and reducing overall testing time.
Data Source
AI summary
Systems, computer program products, and methods are described herein for dynamically determining performance benchmarking parameters based on reinforcement learning. The present invention is configured to implement the first distributed impact simulation model on an application; initiate a reinforcement learning algorithm on the application, wherein initiating further comprises receiving a performance assessment output for the one or more application parameters; initiate an optimization policy generation engine on the performance assessment output associated with the application parameters to generate an optimization to encode the performance assessment output into rewards and costs; initiate an implementation of the optimization policy on the application to maximize an aggregated reward calculated from the second portion of the first set of actions; automatically generate a second distributed impact simulation model using the second set of actions to be implemented on the application parameters; and implement the second distributed impact simulation model on the application.