CAMAL: Optimizing LSM-trees via Active Learning

Abstract

We use machine learning to optimize LSM-tree structure, aiming to reduce the cost of processing various read/write operations. We introduce a new approach CAMAL, which boasts the following features: (1) ML-Aided : CAMAL is the first attempt to apply active learning to tune LSM-tree based key-value stores. The learning process is coupled with traditional cost models to improve the training process; (2) Decoupled Active Learning : backed by rigorous analysis, CAMAL adopts active learning paradigm based on a decoupled tuning of each parameter, which further accelerates the learning process; (3) Easy Extrapolation : CAMAL adopts an effective mechanism to incrementally update the model with the growth of the data size; (4) Dynamic Mode : CAMAL is able to tune LSM-tree online under dynamically changing workloads; (5) Significant System Improvement : By integrating CAMAL into a full system RocksDB, the system performance improves by 28% on average and up to 8x compared to a state-of-the-art RocksDB design.