Hierarchical Multi-Agent Framework for Carbon-Efficient Liquid-Cooled Data Center Clusters
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916610291269632 |
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| author | Sarkar, Soumyendu Naug, Avisek Guillen, Antonio Gundecha, Vineet Gutierrez, Ricardo Luna Ghorbanpour, Sahand Mousavi, Sajad Babu, Ashwin Ramesh Rengarajan, Desik Bash, Cullen |
| author_facet | Sarkar, Soumyendu Naug, Avisek Guillen, Antonio Gundecha, Vineet Gutierrez, Ricardo Luna Ghorbanpour, Sahand Mousavi, Sajad Babu, Ashwin Ramesh Rengarajan, Desik Bash, Cullen |
| contents | 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 such as 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_08337 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hierarchical Multi-Agent Framework for Carbon-Efficient Liquid-Cooled Data Center Clusters Sarkar, Soumyendu Naug, Avisek Guillen, Antonio Gundecha, Vineet Gutierrez, Ricardo Luna Ghorbanpour, Sahand Mousavi, Sajad Babu, Ashwin Ramesh Rengarajan, Desik Bash, Cullen Machine Learning Artificial Intelligence Systems and Control 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 such as 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. |
| title | Hierarchical Multi-Agent Framework for Carbon-Efficient Liquid-Cooled Data Center Clusters |
| topic | Machine Learning Artificial Intelligence Systems and Control |
| url | https://arxiv.org/abs/2502.08337 |