This repository contains the source code for the paper "K2vTune: A Workload-aware Configuration Tuning for RocksDB" published on Information Processing & Management in January 2024. K2vTune is a workload-aware configuration tuning framework, which can recognize the configuration knobs of RocksDB according to the workload type and effectively consider multiple performance metrics using our knob2vec method.
- lifelines
- pytorch == 1.7.0
- python >= 3.8
conda install gxx_linux-64 gcc_linux-64 swig pip install smac
target : target workload number tf : using teacher forcing, if not specify this, the model will be trained by non-teacher forcing train : mode of train eval : mode of train using pre-trained model(.pt) model_path : if using eval mode, add pre-trained model path batch_size : batch size for dataset hidden_size : hidden size of the model lr : learning rate of the model mode : regression model type ['raw', 'dnn', 'gru', 'attngru'] attn_mode : attention tyep ['dot', 'general', 'concat', 'bahdanau'] generation : the counts of generation in Genetic Algorithm pool : size of pool in genetic algorithm optimization : choose optimization algorithm ['ga', 'smac']
Please modify arguments in the bash file. See train.sh
./main.sh train
or
python main.py --target ${target_idx} --tf --train --hidden_size ${hidden_size} --lr ${learning_rate} \
--generation ${generation_num} --pool ${pool_num}
Please modify arguments in the bash file. See eval.sh
./main.sh eval
or
python main.py --target ${target_idx} --tf --eval --model_path ${model_path} \
--generation ${generation_num} --pool ${pool_num}
We set the parameters as follows
hidden_size = 128lr = 0.001generation = 100pool = 128mode = 'attngru'attn_mode = 'general'optimization = 'ga'
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