JointConf: Jointly autotuning configuration parameters for modularized graph databases

Abstract

To support different application scenarios, graph databases (GDBs) usually provide a large number of performance-related parameters for developers. Since manually configuring is both time-consuming and cost-intensive, automatically tuning configurations parameters to achieve a better performance has been an urgent need. Besides, considering various graph management requirements, GDBs begin to utilize the modular architecture to interoperate with a wide range of storage and index backends. Due to the complicated interactions among different modules, sequentially tuning each software with previous solutions may fall into a local optimal and it is necessary to jointly autotune the cross-module configuration parameters. Toward this challenging target, we propose JointConf-a new black-box approach of jointly autotuning configuration parameters for modularized GDBs. To address the formulated highdimensional black-box optimization problem, JointConf utilizes the recently proposed BO_dropout algorithm. Inspired by the dropout algorithm in neural networks, BO_dropout explores efficient dimension dropout to achieve a high-dimensional Bayesian optimization. We evaluate the effectiveness of JointConf on a local distributed JanusGraph cluster with three different graph query benchmark applications and experimental results show its advantages over the four baseline search-based approaches. The necessity of jointly tuning for modularized GDBs is also verified in our experiments.