GreenDirector adds water and carbon impact to workload scheduling
GreenDirector: carbon- and water-aware workload placement for sustainable computing
Distributed, Parallel, and Cluster Computing
Summary
Data centers use lots of electricity, and while some electricity is low in carbon emissions, it can be heavy on water use, which can harm the environment depending on local water scarcity. The authors created an Environmental Score that measures both carbon emissions and water impact considering when and where electricity is used. They also made a Green Score that helps schedule computer jobs to get the most work done with the least environmental harm. They tested this system on real cloud and scientific computing setups, showing it can reduce carbon emissions and handle water usage concerns without slowing down the work.
What this means in practice
- •For data center operators: Schedule computing workloads to reduce combined carbon and water environmental impact based on real-time electricity and local scarcity data.
- •For cloud infrastructure teams: Optimize workload placement across multiple data centers to improve carbon efficiency and manage water stress trade-offs dynamically in production systems.
Authors
Jime Iglesias Blanco, Ignacio Heredia, María Castrillo, Andrei Tsaregorodtsev, Mazen Ezzeddine, Álvaro López García
Abstract
The rapid growth of data center electricity demand, accelerated by AI, makes carbon-only accounting an incomplete measure of computing's environmental impact: low-carbon electricity mixes are often water-intensive, and the resulting harm depends on local, seasonal scarcity rather than on the volume of water consumed. We propose the Environmental Score (ES), a unified, dimensionless index in $[0, 100]$ that jointly captures the carbon footprint and the spatial-temporal, scarcity-weighted water impact of the electricity a workload consumes. It combines real-time, cross-border electricity flow tracing with monthly AWARE2.0 water-scarcity characterization factors, weighting global greenhouse-gas emissions together with the local, seasonal severity of water stress. Building on it, we define the Green Score (GS), a scheduling metric proportional to the useful computational work delivered per unit of real environmental impact, which also accounts for data center power and hardware efficiency. We add both metrics as a green-affinity feature to the GreenDirector schedulers of two production federated infrastructures, the AI4EOSC scientific cloud and the DIRAC workload management system. In AI4EOSC, a cluster-filling experiment over four pan-European providers shows that greener sites are filled first without degrading scheduling latency or end-user experience. In DIRAC, a trace-driven simulation of 133,631 jobs and a preliminary production deployment for the KM3NeT community reduce carbon emissions and improve carbon efficiency by about 40\%, while making the carbon-water trade-off explicit when the lowest-carbon site also carries higher water stress. The results show that hydrological stress can be dynamically weighted into workload placement in live, multi-tenant systems