This project is the code support for the paper ML-SACRE: Machine Learning-based Stable Auto-scaling of Cloud Resources with Efficiency. It contains:
- The code for the presented method, i.e. the ML-SACRE algorithm and its underlying machine learning models.
- The code to run the presented simulation experiments from the paper and generate their results.
pip install poetry
Before installing the project, make sure the requirements are satisfied. Then, from the project root directory, run the following command:
poetry install
Optional (but recommended):
poetry shell
This will activate a virtual environment to run project scripts in.
An example cofiguration can be found in example_configuration.py. It is the configuration used for the simulations in the paper. The input_dirpath and output_dirpath fields need to be filled with real directory paths. The other parameters in the configuration can also be customized.
Training parameters for the time series and PPO agent models can be found (and modified) in the model files themselves: models/time_series.py and models/agent.py respectively. The current parameters are those used in the paper.
The input directory specified in the configuration should contain 3 .csv files: df_train.csv, df_validation.csv and df_test.csv. The dataframe column names should contain a Timestamp column, as well as Requested and Used resource columns matching each resource item in the configuration file, along the following pattern: [Resource name] Requested ([Resource unit]) and [Resource name] Used ([Resource unit]). The separator used is tab (\t). For example:
Timestamp CPU Requested (%) RAM Requested (GB) CPU Used (%) RAM Used (GB)
2023-12-04 00:50:00 100 17 8 5
2023-12-04 01:00:00 100 17 2 5
2023-12-04 01:10:00 100 17 6 5
2023-12-04 01:20:00 100 17 2 5
...
To run a set of simulations, prepare the inputs as described above, and update the configuration file path in the main file main.py. Then run the main script:
python main.py
The output directory as set in the configuration file will contain all results and corresponding plots for each experiment trial (set of simulations), as well as aggregated results and plots.