Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems
arXiv:2607.03324v1 Announce Type: cross Abstract: Eco-friendly energy management for artificial intelligence data centers (AIDCs) is crucial because of the significant increase in energy consumption-induced carbon emissions from AIDCs resulting from the rapid expansion of AI applications. This paper proposes a hierarchical carbon-aware multi-agent reinforcement learning (CA-MARL) framework for robust and efficient operations of AIDCs under uncertainties while ensuring low-carbon operation of power distribution systems. The framework comprises a workload manager (WM) agent and multiple local AI
The rapid expansion of AI applications is driving a significant increase in energy consumption by data centers, making eco-friendly energy management critical for sustainable growth.
This development addresses the critical energy footprint of AI, proposing a solution for carbon-aware operation of AI data centers that can mitigate environmental impact and ensure grid stability.
The operational paradigm for AI data centers is shifting from purely performance-driven to carbon-aware, integrating environmental sustainability and grid resilience into core management strategies.
- · AI data center operators
- · Renewable energy providers
- · Power grid operators
- · AI sustainability tech providers
- · High-carbon energy producers
- · Traditional data center operators (unoptimized)
- · Regions with unstable power grids
Widespread adoption of carbon-aware energy management systems in AI data centers to reduce emissions.
Increased investment in grid infrastructure and renewable energy sources to meet growing, greener data center demands.
New regulations and standards emerge, mandating carbon efficiency for AI infrastructure, shaping future development.
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Read at arXiv cs.AI