Toward Carbon-Aware Data Transfers

The growing adoption of cloud, edge, and distributed computing, as well as the rise in the use of artificial intelligence/machine learning workloads, have created a significant need to measure, monitor, and reduce the carbon emissions associated with these resource-intensive tasks. One significant but often overlooked source of emissions is data transfers over wide-area networks, primarily due to the challenges in accurately measuring the carbon footprint of end-to-end network paths. We introduce a novel mechanism to measure network carbon footprints and propose strategies for optimizing the scheduling of network-intensive tasks. We show that users can achieve significant carbon savings by shifting data transfer tasks across time and geographic regions based on local carbon intensity.