LatentTune: Efficient Tuning of High Dimensional Database Parameters via Latent Representation Learning

2026-02-04

AI Summary

This work addresses the challenges of inefficient, incomplete, and workload-mismatched tuning in high-dimensional database parameter optimization. The authors propose a holistic parameter tuning framework based on implicit representation learning, which constructs a latent space encompassing all configuration parameters, integrates target workload metrics, and leverages data augmentation and high-dimensional compression techniques to achieve efficient and generalizable configuration optimization. Experimental evaluations on MySQL and RocksDB demonstrate that the proposed method significantly outperforms existing baselines: it achieves up to a 1332% performance improvement on RocksDB, and for MySQL, it increases throughput by 11.82% while reducing latency by 46.01%.

Technology Category

• Search and Optimization: Algorithm Configuration
• Machine Learning: Auto ML and Hyperparameter Tuning
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

As data volumes continue to grow, optimizing database performance has become increasingly critical, making the implementation of effective tuning methods essential. Among various approaches, database parameter tuning has proven to be a highly effective means of enhancing performance. Recent studies have shown that machine learning techniques can successfully optimize database parameters, leading to significant performance improvements. However, existing methods still face several limitations. First, they require substantial time to generate large training datasets. Second, to cope with the challenges of highdimensional optimization, they typically optimize only a subset of parameters rather than the full configuration space. Third, they often rely on information from similar workloads instead of directly leveraging information from the target workload. To address these limitations, we propose LatentTune, a novel approach that differs fundamentally from traditional methods. To reduce the time required for data generation, LatentTune incorporates a data augmentation strategy. Furthermore, it constructs a latent space that compresses information from all database parameters, enabling the optimization of the full configuration space. In addition, LatentTune integrates external metric information into the latent space, allowing for precise tuning tailored to the actual target workload. Experimental results demonstrate that LatentTune outperforms baseline models across four workloads on MySQL and RocksDB, achieving up to 1332% improvement for RocksDB and 11.82% throughput gain with 46.01% latency reduction for MySQL.

Problem

• database parameter tuning
• high-dimensional optimization
• workload-specific tuning
• training data generation
• configuration space

Innovation

• Latent Representation Learning
• Database Parameter Tuning
• High-Dimensional Optimization
• Data Augmentation
• Workload-Specific Tuning