Configuration Generation for Enterprise Application Tuning
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Solution Overview
Problem
Optimizing enterprise application performance across its lifecycle is challenging due to changing data nature and numerous configurable parameters, requiring intricate knowledge and resource-intensive experimentation to determine optimal configurations, which is time-consuming and often unfeasible with limited budgets.
Innovation Solution
A computer-implemented method and system that determines optimal system configurations by selecting a subset of configurations for testing, simulating their performance, creating a model based on measured data, and generating configurations for tuning, using techniques like design of experiments, particle swarm optimization, and machine learning to reduce the need for prior knowledge of constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If experiments are performed to measure application performance under different configurations, then optimal configuration can be determined, but computing resources are consumed and time is required for testing
Solution Approach 1:
The patent applies partial action by selecting and testing only a subset of possible configurations rather than exhaustively testing all configurations. The system identifies representative configurations that provide sufficient performance data to build accurate models without requiring complete coverage of the entire configuration space, thus reducing computing resource consumption while maintaining measurement precision.
Solution Approach 2:
The patent uses preliminary action by performing initial experiments to build performance models before actual application deployment. These preliminary measurements are used to create predictive models that can estimate performance without requiring extensive actual testing, thereby reducing the computing resources needed during production use.
2Reliability
If all possible configurations are tested to ensure optimal performance, then best configuration can be identified, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The system tests only a partial set of configurations that are most likely to yield optimal performance. By using design of experiments and predictive modeling, the system identifies representative configurations that provide sufficient information to determine optimal settings without exhaustively testing every possible configuration combination, significantly reducing tuning time while maintaining reliability.
Solution Approach 2:
The patent creates predictive models that copy the behavior of the application under different configurations. Instead of actually testing every configuration, the system builds virtual models that simulate performance outcomes, allowing optimal configuration identification without the time cost of exhaustive physical testing.
3Adaptability or versatility
If extensive configuration testing is performed to account for changing data nature, then optimal performance can be maintained, but budgets and resources may not allow for testing all configurations
Solution Approach 1:
The system performs preliminary configuration analysis and builds predictive models that can adapt to changing data characteristics. By pre-establishing performance models and using them to guide configuration selection, the system can maintain adaptability to data changes without requiring extensive resources for continuous exhaustive testing.
Solution Approach 2:
The patent uses predictive models as virtual copies of the application's performance behavior under different data conditions. These models allow the system to evaluate configuration performance for varying data characteristics without requiring actual testing resources for every scenario, maintaining adaptability within budget constraints.
Data Source
AI summary
Techniques for tuning systems generate configurations that are used to test the systems to determine optimal configurations for the systems. The configurations for a system are generated to allow for effective testing of the system while remaining within budgetary and/or resource constraints. The configurations may be selected to satisfy one or more conditions on their distributions to ensure that a satisfactory set of configurations are tested. Machine learning techniques may be used to create models of systems and those models can be used to determine optimal configurations.