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Repository to the Sizey Paper.

@inproceedings{bader2024Sizey,
  author={Bader, Jonathan and Skalski, Fabian and Lehmann, Fabian and Scheinert, Dominik and Will, Jonathan and Thamsen, Lauritz and Kao, Odej},
  booktitle={2024 IEEE International Conference on Cluster Computing (CLUSTER)}, 
  title={Sizey: Memory-Efficient Execution of Scientific Workflow Tasks}, 
  year={2024},
}

Run Sizey

  1. Create a Python virtual environment and install the dependencies
  2. Run python3 main.py filename alpha softmax error_metric seed
  • filename describes the workflow from the data folder. For instance ./data/trace_methylseq.csv
  • alpha sets the alpha you want to execute Sizey with. It has to be between 0.0 and 1.0
  • interpolation actives the interpolation strategy. It is either False or True. If set to False, the Argmax strategy is used.
  • error_metric defines the XYZ used for ABC. Currently, it is either smoothed_mape or neg_mean_squared_error whereas smoothed_mape should be used and other error metrics might be experimental and change the impact on the RAQ score.
  • seed defines the seed for splitting up the initial data in training and test data and also defines the order of online task input.

Here is an example command: ./data/trace_methylseq.csv 0.0 True smoothed_mape 1996