Carbon-Aware End-to-End Data Movement

TL;DR

This paper introduces a novel mechanism to measure the carbon footprint of network data movement and proposes optimization strategies for scheduling tasks to reduce carbon emissions based on geographic carbon intensity.

Contribution

It presents a new method for measuring network carbon footprints and offers three optimization techniques for scheduling data movement to minimize carbon emissions.

Findings

Effective measurement of network carbon footprint achieved

Scheduling strategies reduce carbon emissions by shifting tasks in time and space

Potential for significant carbon savings in distributed computing environments

Abstract

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.