Towards Workload-Specific Configuration Tuning via Meta-Learning for RocksDB

A persistent key-value store, RocksDB, is adapt-able to various workloads and provides fast and low-latency storage for devices that are utilized by numerous applications. RocksDB has been introduced with numerous configuration options for customization and performance optimization. Unfor-tunately, determining an optimal configuration for each given workload remains challenging due to the overwhelming number of options. This complexity is compounded by different types of workloads, thereby requiring efficient configuration tuning. Recent studies have approached automatic tuning techniques to solve this problem by applying reinforcement learning approaches or transferring prior knowledge to predictive models in order to tune unobserved target workloads. However, the former method is time-consuming, and the latter results in unstable optimal performance according to the accuracy of the predictive models. The models trained with prior knowledge, estimate RocksDB performance of given configurations on the target workload, where those workload mismatches degrade tuning performance. To address these challenges, we propose MetaTune, which introduces a meta learner, which is a meta-learning technique, to train a workload -specific predictive model. MetaTune effectively transfers prior knowledge and effi-ciently fine-tunes the model for new workloads. We conducted a comparative analysis of MetaTune with the state-of-the-art baselines across a heterogeneous set of workloads. MetaTune achieved 3.78% to 53.25% improvement in tuning performance compared to the most recent baseline.