  Climate change due to increasing carbon emissions by human activities has
been identified as one of the most critical threat to Earth. Carbon
neutralization, as a key approach to reverse climate change, has triggered the
development of new regulations to enforce the economic activities toward low
carbon solutions. Computing networks that enable users to process
computation-intensive tasks contribute huge amount of carbon emissions due to
rising energy consumption. To analyze the achievable reduction of carbon
emissions by a scheduling policy, we first propose a novel virtual queueing
network model that captures communication and computing procedures in networks.
To adapt to highly variable and unpredictable nature of renewable energy
utilized by computing networks (i.e., carbon intensity of grid varies by time
and location), we propose a novel carbon-intensity based scheduling policy that
dynamically schedules computation tasks over clouds via the drift-plus-penalty
methodology in Lyapunov optimization. Our numerical analysis using real-world
data shows that the proposed policy achieves 54% reduction on the cumulative
carbon emissions for AI model training tasks compared to the queue-length based
policy.

Distributed, Parallel, and Cluster Computing