This study dissects the knob tuning pipeline into five components: Workload Characterization, Feature Pruning, Knowledge from Experience, Configuration Recommendation, improving tuning efficiency.
Workload Characterization models a workload through logical queries and runtime metrics, providing essential performance indicators to enhance the tuning process.
Feature Pruning reduces the execution time by eliminating irrelevant features at both the workload and configuration levels, optimizing the overall search process.
Configuration Recommendation leverages techniques like Bayesian Optimization and Neural Networks to provide tailored configurations for workload-specific performance enhancement.
#configuration-tuning #workload-characterization #feature-pruning #machine-learning #optimization-techniques
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