Software Configuration Optimization via Neural Network Parameter Tuning
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Solution Overview
Problem
Existing software systems face performance optimization challenges due to varying customer data and usage habits, making it difficult to set optimal configuration parameters, as a unified factory setting cannot account for diverse customer needs and changing data types.
Innovation Solution
A system and method for automatic configuration optimization, which involves a configuration component that compares performance indicia from two processing systems, one configured with initial parameter values and another with altered values, to determine and implement performance-improving parameter settings, potentially using neural networks for parameter adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a unified factory setting is used for configuration parameters, then the system is easy to deploy and maintain, but the system cannot achieve optimal performance for diverse customer needs and varying data types
Solution Approach 1:
The system automatically optimizes its own configuration parameters by monitoring performance metrics and adjusting parameters without external intervention. The software application self-adjusts configuration parameters based on observed performance data, eliminating the need for manual configuration management while achieving optimal performance for diverse customer needs
Solution Approach 2:
The configuration parameters are made dynamic rather than static, allowing them to change automatically based on system conditions and performance metrics. The system continuously adapts parameters such as buffer sizes, connection pool settings, and processing thresholds to match current operational requirements, enabling both adaptability and simplified management
2Productivity
If software parameter values are manually configured, then configuration is simple and fast, but the system cannot achieve optimal performance for varying data types and usage patterns
Solution Approach 1:
The system performs preliminary configuration optimization by pre-testing multiple parameter combinations in a test environment before deployment. Performance metrics are collected and analyzed in advance, allowing the system to start with pre-optimized parameters rather than requiring time-consuming manual tuning after deployment
Solution Approach 2:
The system implements continuous feedback loops where performance metrics are monitored, analyzed, and used to automatically adjust configuration parameters. This closed-loop control enables the system to learn from operational data and continuously improve performance without manual intervention, reducing both initial setup time and ongoing optimization time
3Measurement precision
If every possible combination of software parameter values is tested, then the optimal configuration can be found, but the testing process takes an excessively long period of time
Solution Approach 1:
Instead of exhaustively testing all possible parameter combinations, the system intelligently identifies and tests only the most relevant parameter changes based on performance metrics and usage patterns. The system focuses computational resources on parameters that have the greatest impact on performance, achieving accurate optimization without exhaustive testing
Solution Approach 2:
The system performs partial testing by evaluating a selective subset of parameter combinations rather than all possible combinations. By using performance metrics and usage data to guide which parameter sets to test, the system achieves sufficient optimization accuracy with significantly reduced testing time, applying just enough testing effort to reach optimal performance
Data Source
AI summary
Software applications may be installed on a variety of platforms and/or process a variety of data or data in a variety of structures. As a result, optimizing a system to maximize performance often depends on a specific utilization and the specific data being processed. Systems and methods are provided herein to compare the performance of one processing logic, often a live or production environment to a test environment that varies the performance parameters. The variations may be provided by a machine learning algorithm, such as neural network, and produce an improved set of parameters that may then be applied to the production environment.