Hierarchical Multi-Agent Framework for Carbon-Efficient Liquid-Cooled Data Center Clusters

Abstract

Reducing the environmental impact of cloud computing requires efficient workload distribution across geographically dispersed Data Center Clusters (DCCs) and simultaneously optimizing liquid and air (HVAC) cooling with time shift of workloads within individual data centers (DC). This paper introduces Green-DCC, which proposes a Reinforcement Learning (RL) based hierarchical controller to optimize both workload and liquid cooling dynamically in a DCC. By incorporating factors like weather, carbon intensity, and resource availability, Green-DCC addresses realistic constraints and interdependencies. We demonstrate how the system optimizes multiple data centers synchronously, enabling the scope of digital twins, and compare the performance of various RL approaches based on carbon emissions and sustainability metrics while also offering a framework and benchmark simulation for broader ML research in sustainability.

Cite

Text

Sarkar et al. "Hierarchical Multi-Agent Framework for Carbon-Efficient Liquid-Cooled Data Center Clusters." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I28.35370

Markdown

[Sarkar et al. "Hierarchical Multi-Agent Framework for Carbon-Efficient Liquid-Cooled Data Center Clusters." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/sarkar2025aaai-hierarchical/) doi:10.1609/AAAI.V39I28.35370

BibTeX

@inproceedings{sarkar2025aaai-hierarchical,
  title     = {{Hierarchical Multi-Agent Framework for Carbon-Efficient Liquid-Cooled Data Center Clusters}},
  author    = {Sarkar, Soumyendu and Naug, Avisek and Guillen, Antonio and Gundecha, Vineet and Gutiérrez, Ricardo Luna and Ghorbanpour, Sahand and Mousavi, Sajad and Babu, Ashwin Ramesh and Rengarajan, Desik and Bash, Cullen E.},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2025},
  pages     = {29694-29696},
  doi       = {10.1609/AAAI.V39I28.35370},
  url       = {https://mlanthology.org/aaai/2025/sarkar2025aaai-hierarchical/}
}