Centrum: Model-based Database Auto-tuning with Minimal Distributional Assumptions
Yuanhao Lai, Pengfei Zheng, Chenpeng Ji, Yan Li, Songhan Zhang, Rutao Zhang, Zhengang Wang, Yunfei Du
TL;DR
This work addresses the limitations of GP-BO for DBMS auto-tuning by introducing Centrum, a distribution-free model-based tuner that fuses gradient-boosting ensembles with conformal inference. Centrum replaces Gaussian assumptions with a two-phase Stochastic Gradient Boosting Ensemble (SGBE) surrogate and a locally adaptive, conformal uncertainty framework, enabling distribution-free point and interval estimates used in a distribution-free EI acquisition. The approach co-optimizes point and interval accuracy via SR2 and NAIS and a two-phase training with a WIS-driven ensemble refinement, achieving strong empirical gains: ahead of 21 SOTA tuners in both accuracy and efficiency across MySQL and PostgreSQL benchmarks, with up to a 4.2× speedup in tuning time. The results demonstrate practical impact for DBMS auto-tuning in cloud environments, offering robust performance under non-continuous, non-Gaussian, and non-stationary measurement regimes.
Abstract
Gaussian-Process-based Bayesian optimization (GP-BO), is a prevailing model-based framework for DBMS auto-tuning. However, recent work shows GP-BO-based DBMS auto-tuners significantly outperformed auto-tuners based on SMAC, which features random forest surrogate models; such results motivate us to rethink and investigate the limitations of GP-BO in auto-tuner design. We find the fundamental assumptions of GP-BO are widely violated when modeling and optimizing DBMS performance, while tree-ensemble-BOs (e.g., SMAC) can avoid the assumption pitfalls and deliver improved tuning efficiency and effectiveness. Moreover, we argue that existing tree-ensemble-BOs restrict further advancement in DBMS auto-tuning. First, existing tree-ensemble-BOs can only achieve distribution-free point estimates, but still impose unrealistic distributional assumptions on uncertainty estimates, compromising surrogate modeling and distort the acquisition function. Second, recent advances in gradient boosting, which can further enhance surrogate modeling against vanilla GP and random forest counterparts, have rarely been applied in optimizing DBMS auto-tuners. To address these issues, we propose a novel model-based DBMS auto-tuner, Centrum. Centrum improves distribution-free point and interval estimation in surrogate modeling with a two-phase learning procedure of stochastic gradient boosting ensembles. Moreover, Centrum adopts a generalized SGBE-estimated locally-adaptive conformal prediction to facilitate a distribution-free uncertainty estimation and acquisition function. To our knowledge, Centrum is the first auto-tuner to realize distribution-freeness, enhancing BO's practicality in DBMS auto-tuning, and the first to seamlessly fuse gradient boosting ensembles and conformal inference in BO. Extensive physical and simulation experiments on two DBMSs and three workloads show Centrum outperforms 21 SOTA methods.