Software Tuning via Dynamic Threshold Algorithm Selection
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Solution Overview
Problem
Existing software tuning methods require human intervention and complex statistical techniques, making them inefficient and prone to errors in optimizing software performance across varying environments.
Innovation Solution
A mechanism that allows software to automatically tune itself by dynamically adjusting threshold values and selecting the most suitable algorithms at runtime, based on simple comparisons with threshold values, to achieve optimal performance without human interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex statistical techniques are used for automatic performance tuning, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent replaces complex statistical techniques with simple threshold comparisons that are computationally inexpensive and easy to implement. Instead of using heavy statistical models, the system uses lightweight threshold values that can be quickly calculated and applied, reducing the complexity of the tuning system while maintaining effectiveness.
Solution Approach 2:
The patent changes the approach from complex statistical analysis to simple parameter-based threshold comparisons. By focusing on key performance parameters and establishing threshold values for them, the system achieves precise performance measurement without requiring complex statistical techniques, thus resolving the contradiction between precision and complexity.
2Manufacturing precision
If manual parameter tuning is performed by specialists, then manufacturing precision is improved, but loss of time increases
Solution Approach 1:
The patent implements self-service by enabling the software system to automatically tune its own parameters without requiring specialist intervention. The system uses simple threshold comparisons to automatically determine optimal configurations, eliminating the time-consuming manual tuning process while maintaining the precision that previously required expert knowledge.
Solution Approach 2:
The patent performs preliminary action by pre-calculating threshold values that can be applied automatically. Instead of requiring specialists to perform manual tuning when needed, the system has threshold values prepared in advance that can be quickly applied to achieve optimal configuration, thus reducing the time loss associated with manual tuning.
3Device complexity
If static configuration is used for software parameters, then device complexity is reduced, but adaptability decreases
Solution Approach 1:
The patent introduces dynamics by allowing the system to adapt its configuration based on runtime conditions. Instead of using fixed static configuration, the system dynamically compares current performance parameters against threshold values and adjusts settings accordingly. This dynamic approach maintains simplicity while significantly improving environmental adaptability.
4Manufacturing precision
If exhaustive compile-time search is performed, then manufacturing precision is improved, but loss of time increases
Solution Approach 1:
The patent applies partial action by using simple threshold comparisons instead of performing exhaustive searches. Rather than exploring all possible implementations during compile-time, the system uses pre-established threshold values to make sufficient decisions about optimal configurations. This partial approach achieves adequate optimization precision without the excessive time cost of exhaustive search.
Data Source
AI summary
Method and computer system for software tuning. A computer system stores variables (210) for storing at least one threshold value for at least one parameter (P1) influencing the performance of a software application (200) with regards to a specific task. A threshold evaluator (220) compares (430) the at least one threshold value to at least one corresponding current value allowing the software application (200) to select (440) an algorithm (A1) from a plurality of algorithms (A1 to AN) for performing the task in accordance with the result of comparison.