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