Authors:
(1) Limeng Zhang, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia;
(2) M. Ali Babar, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia. Table of Links Abstract and 1 Introduction 1.1 Configuration Parameter Tuning Challenges and 1.2 Contributions 2 Tuning Objectives 3 Overview of Tuning Framework 4 Workload Characterization and 4.1 Query-level Characterization 4.2 Runtime-based Characterization 5 Feature Pruning and 5.1 Workload-level Pruning 5.2 Configuration-level Pruning 5.3 Summary 6 Knowledge from Experience 7 Configuration Recommendation and 7.1 Bayesian Optimization 7.2 Neural Network 7.3 Reinforcement Learning 7.4 Search-based Solutions 8 Experimental Setting 9 Related Work 10 Discussion and Conclusion, and References 3 OVERVIEW OF TUNING FRAMEWORK In this study, we break down the entire knob tuning pipeline into five essential components: Workload Characterization, Feature Pruning, Knowledge from Experience, Configuration Recommendation as depicted in Fig. 1. The initial phase, Workload Characterization, is crafted to comprehensively model a workload, utilizing either logical-level queries or embedding runtime metrics for a dynamic understanding of performance indicators Feature Pruning, applied both at the workload-level to minimize execution time and at the configuration-level to streamline the search space, is strategically employed to enhance efficiency through the application of pruning techniques. Knowledge from Experience draws upon historical insights, enabling the tuning algorithm to efficiently converge towards optimal configurations. Finally, Configuration Recommendation involves utilizing diverse techniques, including Bayesian Optimization, Neural networks, Reinforcement learning, and Search-based methods, to generate configurations finely tailored to the specific characteristics of the workload. This paper is available on arxiv under CC BY 4.0 DEED. Authors: (1) Limeng Zhang, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia; (2) M. Ali Babar, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia. Authors: Authors: (1) Limeng Zhang, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia; (2) M. Ali Babar, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 1.1 Configuration Parameter Tuning Challenges and 1.2 Contributions 1.1 Configuration Parameter Tuning Challenges and 1.2 Contributions 2 Tuning Objectives 2 Tuning Objectives 3 Overview of Tuning Framework 3 Overview of Tuning Framework 4 Workload Characterization and 4.1 Query-level Characterization 4 Workload Characterization and 4.1 Query-level Characterization 4.2 Runtime-based Characterization 4.2 Runtime-based Characterization 5 Feature Pruning and 5.1 Workload-level Pruning 5 Feature Pruning and 5.1 Workload-level Pruning 5.2 Configuration-level Pruning 5.2 Configuration-level Pruning 5.3 Summary 5.3 Summary 6 Knowledge from Experience 6 Knowledge from Experience 7 Configuration Recommendation and 7.1 Bayesian Optimization 7 Configuration Recommendation and 7.1 Bayesian Optimization 7.2 Neural Network 7.2 Neural Network 7.3 Reinforcement Learning 7.3 Reinforcement Learning 7.4 Search-based Solutions 7.4 Search-based Solutions 8 Experimental Setting 8 Experimental Setting 9 Related Work 9 Related Work 10 Discussion and Conclusion, and References 10 Discussion and Conclusion, and References 3 OVERVIEW OF TUNING FRAMEWORK In this study, we break down the entire knob tuning pipeline into five essential components: Workload Characterization, Feature Pruning, Knowledge from Experience, Configuration Recommendation as depicted in Fig. 1. The initial phase, Workload Characterization, is crafted to comprehensively model a workload, utilizing either logical-level queries or embedding runtime metrics for a dynamic understanding of performance indicators Feature Pruning, applied both at the workload-level to minimize execution time and at the configuration-level to streamline the search space, is strategically employed to enhance efficiency through the application of pruning techniques. Knowledge from Experience draws upon historical insights, enabling the tuning algorithm to efficiently converge towards optimal configurations. Finally, Configuration Recommendation involves utilizing diverse techniques, including Bayesian Optimization, Neural networks, Reinforcement learning, and Search-based methods, to generate configurations finely tailored to the specific characteristics of the workload. This paper is available on arxiv under CC BY 4.0 DEED. This paper is available on arxiv under CC BY 4.0 DEED. available on arxiv

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