CEO-DC: An Actionable Framework to Close the Carbon Gap in HPC Data Centers
🤖 AI Summary
The carbon emissions of high-performance computing (HPC) data centers are growing unsustainably, driven by AI and scientific computing workloads. Method: This study develops a multi-objective decision-making framework integrating economic incentives and carbon emissions, combining life-cycle assessment, cost–benefit analysis, and carbon footprint quantification to model trade-offs among hardware replacement cycles, computational demand growth, and carbon pricing. Contribution/Results: We propose a procurement-based platform replacement strategy, revealing that a four-year refresh cycle reduces cumulative emissions but fails to meet demand growth in over 44% of scenarios—requiring economic incentives in more than 72%. Moreover, prevailing national carbon prices are generally insufficient to trigger low-carbon infrastructure upgrades. Our framework provides an actionable modeling tool and evidence-based policy guidance for sustainable HPC infrastructure planning.
Application Category
📝 Abstract
The rapid expansion of data centers (DCs) to support large-scale AI and scientific workloads is driving unsustainable growth in energy consumption and greenhouse gas emissions. While successive generations of hardware platforms have improved performance and energy efficiency, the question remains whether new, more efficient platforms can realistically offset the rising emissions associated with increasing demand. Prior studies often overlook the complex trade-offs in such transitions by failing to account for both the economic incentives and the projected compute demand growth over the operational lifetime of the devices. In response, we present CEO-DC, an integrated model and decision-making methodology for Carbon and Economy Optimization in Data Centers. CEO-DC models the competing forces of cost, carbon, and compute demand to guide optimal platform procurement and replacement strategies. We propose metrics to steer procurement, platform design, and policy decisions toward sustainable DC technologies. Given current platform trends, our AI case study using CEO-DC shows that upgrading legacy devices on a 4-year cycle reduces total emissions. However, these upgrades fail to scale with DC demand growth trends without increasing total emissions in over 44% of cases, and require economic incentives for adoption in over 72%. Furthermore, current carbon prices are insufficient to motivate upgrades in 9 out of the 14 countries with the highest number of DCs globally. We also find that optimizing platforms for energy efficiency at the expense of latency can increase the carbon price required to justify their adoption. In summary, CEO-DC provides actionable insights for DC architects, platform designers, and policymakers by timing legacy platform upgrades, constraining DC growth to sustainable levels, optimizing platform performance-to-cost ratios, and increasing incentives.
Problem
Research questions and friction points this paper is trying to address.
Address unsustainable energy growth in HPC data centers
Balance cost, carbon, and compute demand trade-offs
Optimize platform upgrades for emissions reduction
Innovation
Methods, ideas, or system contributions that make the work stand out.
Integrated model for carbon and economy optimization
Metrics for sustainable procurement and design
Optimal timing for legacy platform upgrades
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