Domain Knowledge Optimization Engine for System Configuration
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Solution Overview
Problem
Performance tuning systems face challenges due to the difficulty of manually optimizing parameters, requiring extensive knowledge and being time-consuming, especially when dealing with black-box tunable parameters optimization systems that do not consider domain knowledge, leading to slow convergence and suboptimal configurations.
Innovation Solution
A computer-implemented method and system that utilizes domain knowledge requirements to generate effective performance metrics and optimize target system configurations by integrating a domain knowledge manager, effective metric generator, and optimization engine, which scores domain knowledge requirements and adjusts tunable parameters to improve system performance for throughput and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual tuning of optimization parameters is performed, then system performance can be optimized, but the process becomes time-consuming and requires extensive knowledge
Solution Approach 1:
The system performs self-tuning by automatically identifying and adjusting optimization parameters without human intervention. The automated system analyzes system behavior, evaluates performance metrics, and modifies configuration parameters autonomously, eliminating the need for manual expert tuning while reducing time consumption.
Solution Approach 2:
The patent replaces manual mechanical tuning processes with automated computational systems. Instead of human experts manually adjusting parameters based on domain knowledge, an automated optimization engine uses algorithms and machine learning to systematically explore parameter spaces and identify optimal configurations.
2Extent of automation
If black-box tunable parameters optimization is performed without domain knowledge, then automation is achieved, but convergence speed decreases and configurations become suboptimal
Solution Approach 1:
The system performs preliminary actions by incorporating domain knowledge and constraints into the optimization process before actual parameter tuning begins. Pre-defined performance metrics, system characteristics, and domain-specific rules are established in advance to guide the automated optimization, enabling faster convergence to meaningful solutions.
Solution Approach 2:
The patent dynamically changes optimization parameters based on domain knowledge and observed system behavior. The system adjusts not only the target parameters being optimized but also the optimization algorithm's own parameters (such as search space boundaries, evaluation criteria, and convergence thresholds) to improve efficiency and solution quality.
3Extent of automation
If black-box tunable parameters optimization is performed without domain knowledge, then automation is achieved, but optimal configurations are not reached
Solution Approach 1:
The system implements continuous feedback loops where performance metrics are monitored, evaluated against domain knowledge and constraints, and used to guide subsequent optimization iterations. The automated system learns from observed outcomes and adjusts its parameter selection and adjustment strategies to progressively improve configuration quality while maintaining automation.
Data Source
AI summary
A computer-implemented method, system, and computer program product are provided for optimization with domain knowledge requirements. The method includes receiving, by a processor device, domain knowledge requirements for a target system. The method also includes defining, by a domain knowledge manager, a status of the domain knowledge employing a factor responsive to the domain knowledge. The method additionally includes computing, by an effective metric generator, effective performance metrics responsive to the status of the domain knowledge requirements and real performance metrics. The method further includes generating, by an optimization engine, a target system configuration responsive to the effective performance metrics and the real performance metrics. The method also includes improving the target system by changing a state of a function in the target system responsive to the target system configuration.