Sustainability of Data Center Digital Twins with Reinforcement Learning
<|reference_start|>Sustainability of Data Center Digital Twins with Reinforcement Learning: The rapid growth of machine learning (ML) has led to an increased demand for computational power, resulting in larger data centers (DCs) and higher energy consumption. To address this issue and reduce carbon emissions, intelligent design and control of DC components such as IT servers, cabinets, HVAC cooling, flexible load shifting, and battery energy storage are essential. However, the complexity of designing and controlling them in tandem presents a significant challenge. While some individual components like CFD-based design and Reinforcement Learning (RL) based HVAC control have been researched, there's a gap in the holistic design and optimization covering all elements simultaneously. To tackle this, we've developed DCRL-Green, a multi-agent RL environment that empowers the ML community to design data centers and research, develop, and refine RL controllers for carbon footprint reduction in DCs. It is a flexible, modular, scalable, and configurable platform that can handle large High Performance Computing (HPC) clusters. Furthermore, in its default setup, DCRL-Green provides a benchmark for evaluating single as well as multi-agent RL algorithms. It easily allows users to subclass the default implementations and design their own control approaches, encouraging community development for sustainable data centers. Open Source Link: https://github.com/HewlettPackard/dc-rl<|reference_end|>
@article{sarkar2024sustainability,
    title={Sustainability of Data Center Digital Twins with Reinforcement Learning},
    author={Soumyendu Sarkar, Avisek Naug, Antonio Guillen, Ricardo Luna, Vineet
  Gundecha, Ashwin Ramesh Babu, Sajad Mousavi},
    journal={Proceedings of the AAAI Conference on Artificial Intelligence,
  vol. 38, no. 20, pp. 22322-22330, Mar. 2024},
    year={2024},
    doi={10.1609/aaai.v38i20.30238},
    archivePrefix={arXiv},
    eprint={2404.10786},
    primaryClass={cs.DC cs.AI cs.LG cs.MA cs.SY eess.SY}
}