CAMAL: Optimizing LSM-trees via Active Learning
<|reference_start|>CAMAL: Optimizing LSM-trees via Active Learning: 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.<|reference_end|>
@article{yu2024camal:,
    title={CAMAL: Optimizing LSM-trees via Active Learning},
    author={Weiping Yu, Siqiang Luo, Zihao Yu, Gao Cong},
    journal={arXiv preprint arXiv:2409.15130},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.15130},
    primaryClass={cs.DB cs.AI cs.LG}
}