Carbon-Aware End-to-End Data Movement
<|reference_start|>Carbon-Aware End-to-End Data Movement: The latest trends in the adoption of cloud, edge, and distributed computing, as well as a rise in applying AI/ML workloads, have created a need to measure, monitor, and reduce the carbon emissions of these compute-intensive workloads and the associated communication costs. The data movement over networks has considerable carbon emission that has been neglected due to the difficulty in measuring the carbon footprint of a given end-to-end network path. We present a novel network carbon footprint measuring mechanism and propose three ways in which users can optimize scheduling network-intensive tasks to enable carbon savings through shifting tasks in time, space, and overlay networks based on the geographic carbon intensity.<|reference_end|>
@article{goldverg2024carbon-aware,
    title={Carbon-Aware End-to-End Data Movement},
    author={Jacob Goldverg, Hasibul Jamil, Elvis Rodriguez and Tevfik Kosar},
    journal={arXiv preprint arXiv:2406.09650},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.09650},
    primaryClass={cs.NI}
}