Carbon-Neutralized Task Scheduling for Green Computing Networks

TL;DR

This paper introduces a novel scheduling policy for green computing networks that significantly reduces carbon emissions by dynamically adapting to renewable energy variability, demonstrated through real-world data analysis.

Contribution

It proposes a new virtual queueing model and a carbon-intensity based scheduling policy using Lyapunov optimization for eco-friendly task management.

Findings

Achieves 54% reduction in cumulative carbon emissions for AI training tasks.

Effectively adapts to variable renewable energy sources.

Outperforms traditional queue-length based policies.

Abstract

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.