GREEN: Carbon-efficient Resource Scheduling for Machine Learning Clusters

This paper explores the problem of scheduling machine Learning (ML) jobs while also taking into account the reduction of carbon emissions in the cluster. Traditional cluster schedulers for ML jobs mainly focus on optimizing job completion time (JCT), but do not consider the environmental impact of their decisions, resulting in a suboptimal carbon footprint. To address this issue, we propose GREEN, an ML cluster scheduler that is both time-efficient and carbon-efficient. At its core, GREEN uses a unique carbon-aware scheduling algorithm that reduces carbon footprint with minimized impact on JCT. Additionally, it leverages the temporal flexibility of ML jobs to reduce carbon emissions by shifting workloads to less carbon-intensive times, while still maintaining overall daily capacity. Our experiments using real ML jobs workload demonstrate that GREEN can achieve up to 41.2% reduction in cluster-wide carbon footprint and 12% reduction in peak power consumption, while incurring 3.6%-5.9% time efficiency tradeoff compared to existing methods.