  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.
