This repository contains datasets, simulation code, and analysis notebooks used in the paper "Let's Wait Awhile: How Temporal Workload Shifting Can Reduce Carbon Emissions in the Cloud":
data/*: Energy production and carbon intensity datasets for the regions Germany, Great Britain, France (all via the ENTSO-E Transparency Platform) and California (via California ISO) for the entire year 2020 +-10 days.compute_carbon_intensity.py: The script used to convert energy production to carbon intensity data using energy source carbon intensity values provided by an IPCC study.simulate.py: A simulator to experimentally evaluate temporal workload shifting approaches in data centers with the goal to consume low-carbon energy.analysis.ipynb: Notebook used to analyze the carbon intensity data.evaluation.ipynb: Notebook used to analyze the simulation results.
For executing the code you need to install the libraries listed in environment.yml, e.g. by using a conda environment.
If you use any datasets or code from this repository, please reference our publication:
- Philipp Wiesner, Ilja Behnke, Dominik Scheinert, Kordian Gontarska, and Lauritz Thamsen. "Let's Wait Awhile: How Temporal Workload Shifting Can Reduce Carbon Emissions in the Cloud" In the Proceedings of the 22nd International Middleware Conference, ACM, 2021.
BibTeX:
@inproceedings{Wiesner_LetsWaitAwhile_2021,
author={Wiesner, Philipp and Behnke, Ilja and Scheinert, Dominik and Gontarska, Kordian and Thamsen, Lauritz},
booktitle={Middleware'21: 22nd International Middleware Conference},
title={Let's Wait Awhile: How Temporal Workload Shifting Can Reduce Carbon Emissions in the Cloud},
publisher = {{ACM}},
year={2021},
doi={10.1145/3464298.3493399}
}