Application Configuration Tuning via Performance Modeling
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Solution Overview
Problem
Optimizing enterprise and other application configurations to ensure optimal performance throughout their life cycle is challenging due to changing data nature and numerous configurable parameters, requiring intricate knowledge and resource-intensive testing to determine the best configuration under varying workloads.
Innovation Solution
A computer-implemented method and system that determines optimal system configurations by selecting a set of configurations, simulating their performance, creating a model based on measured performance, and generating configurations for tuning, using techniques like design of experiments and particle swarm optimization to reduce the need for prior knowledge of constraints and optimize resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive testing of all configurations is performed to determine optimal performance, then measurement precision is improved, but loss of time and use of energy increase significantly
Solution Approach 1:
The patent applies partial action by testing only a selected subset of configurations rather than all possible configurations. The system identifies and tests representative configurations that are most likely to reveal performance characteristics, thereby achieving sufficient measurement precision without the prohibitive time cost of exhaustive testing.
Solution Approach 2:
The patent uses preliminary action by performing initial testing to create a performance model before making configuration recommendations. This preliminary modeling phase identifies promising configuration regions, allowing subsequent testing to focus on specific areas rather than blindly testing all configurations systematically.
2Adaptability or versatility
If a large number of configurations are tested to account for changing data nature and workload variations, then adaptability is improved, but device complexity and loss of time increase
Solution Approach 1:
The patent applies dynamics by creating adaptive performance models that evolve as new testing data becomes available. The system continuously refines its understanding of configuration-performance relationships based on observed workloads and data characteristics, allowing it to adapt to changing conditions without requiring manual reconfiguration or complex static testing frameworks.
Solution Approach 2:
The system performs self-service by automatically selecting which configurations to test next based on its current performance model and observed workload patterns. This self-directed testing strategy eliminates the need for complex external test management and allows the system to autonomously adapt to changing conditions by focusing testing efforts where they are most needed.
3Manufacturing precision
If extensive expertise is required to determine optimal configurations under different workloads, then manufacturing precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent introduces an intermediary performance model that acts as a bridge between raw testing data and configuration recommendations. This model encapsulates complex performance characteristics and relationships, translating them into actionable insights that can be used by operators without requiring deep expertise in the underlying system complexities.
Solution Approach 2:
The patent replaces manual expert analysis with an automated performance modeling system. Instead of relying on human experts to interpret complex performance data and recommend configurations, the system uses computational models to automatically analyze testing results and generate configuration recommendations, thereby eliminating the need for specialized human expertise while maintaining high precision.
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.