Automated Configuration Parameter Optimization for Application Overhead
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Solution Overview
Problem
Configuring parameters for computer programs is complex and time-consuming, as users must specify multiple parameters, and existing methods do not effectively address processing overhead and optimization across multiple applications.
Innovation Solution
A method that estimates the cost and improvement of invoking applications to determine optimal configuration parameter frequencies, which balances overhead and performance, and groups applications to reduce testing overhead, optimizing parameters across multiple applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually configure program parameters, then configuration flexibility is achieved, but configuration time and complexity increase significantly
Solution Approach 1:
The system performs self-configuration by automatically determining optimal parameter values through performance modeling and analysis, eliminating the need for manual user configuration while maintaining flexibility through data-driven decision making
Solution Approach 2:
The system dynamically adjusts configuration parameters based on modeled performance characteristics and actual system conditions, transforming static manual configuration into dynamic automated optimization
2Loss of time
If default values are set by program manufacturer, then configuration time is reduced, but performance optimization is compromised due to inability to account for specific deployment conditions
Solution Approach 1:
The system performs preliminary performance modeling and analysis before configuration, establishing baseline characteristics and predicting optimal parameters based on deployment context, workload patterns, and system architecture
Solution Approach 2:
The system continuously monitors actual system performance and uses this feedback to refine and adjust configuration parameters, creating a closed-loop optimization that adapts to changing conditions
3Reliability
If frequent application invocations are performed to test configuration parameters, then parameter optimization is improved, but processing overhead increases significantly
Solution Approach 1:
The system performs partial invocations and selective testing based on predicted improvement potential, invoking applications only when configuration changes are likely to yield beneficial results, avoiding unnecessary overhead
4Reliability
If all applications are tested individually for configuration optimization, then comprehensive parameter tuning is achieved, but testing complexity and overhead increase
Solution Approach 1:
The system merges similar applications into equivalence classes based on shared behavior patterns and performance characteristics, allowing configuration parameters to be determined once and applied across multiple applications, reducing testing complexity
Data Source
AI summary
Techniques are disclosed for automatically determining one or more configuration parameters associated with one or more programs executable by a computing system. For example, in a system including at least one application and at least one parameter which affects a behavior of the at least one application, a method for determining a frequency for invoking the at least one application includes the following steps. The method estimates a cost which includes overhead resulting from invoking the at least one application. The method estimates an improvement in the behavior resulting from modifying the at least one parameter based on invoking the at least one application. The method selects the frequency based on the cost and the improvement.