High-Dimensional Bayesian Optimization with Multi-Task Learning for RocksDB
Sami Alabed, Eiko Yoneki · 2021
RocksDB is a general-purpose embedded key-value store used in multiple different settings. Its versatility comes at the cost of complex tuning configurations. This paper investigates maximizing the throughput of RocksDB 10 operations by auto-tuning ten parameters of varying ranges. Off-the-shelf optimizers struggle with high-dimensional problem spaces and require a large number of training samples.