VASIM (Vertical Autoscaling SIMulator) is a tool designed to address the complexities involved in assessing autoscaling algorithms.
VASIM is designed for testing recommendation algorithms, with a particular focus on CPU usage in VMs and Kubernetes pods. The toolkit replicates common components found in autoscaler architectures, including the controller, metrics collector, recommender, and resource updater. It enables a comprehensive simulation of the entire autoscaling system’s behavior, with the flexibility to customize various parameters.
If you are writing an academic paper, you can cite this work as:
@inproceedings{pavlenko2024vasim,
author = {Pavlenko, Anna and Saur, Karla and Zhu, Yiwen and Kroth, Brian and Cahoon, Joyce and Camacho-Rodríguez, Jesús},
title = {VASIM: Vertical Autoscaling Simulator Toolkit},
booktitle = {IEEE International Conference on Data Engineering (ICDE 2024)},
year = {2024},
month = {May},
}Our documentation and working example is in our notebook that shows how to get started simulating your data traces and tuning parameters. Our example provides the 3 things needed to run VASim: (1) a data csv file, (2) an autoscaling algorithm, and (3) a metadata.json file of parameters. Details of all of these can be found in the notebook.
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Within each folder, there is a README explaining the code.
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For additional usage examples, see any test in the tests folder that starts with
test_e2e. -
Please see our blog post or research paper for more details!
- Anna Pavlenko (@apavlen, primary implementation)
- Karla Saur (@ksaur, editing, maintaining)
- Yiwen Zhu (@zyw400)
- Brian Kroth (@bpkroth)
- Joyce Cahoon (@jyuu)
- Jesús Camacho Rodríguez (@jcamachor)
Please see CONTRIBUTING.md for techinical details.
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