Sustainability of Data Center Digital Twins with Reinforcement Learning


  • Soumyendu Sarkar Hewlett Packard Enterprise
  • Avisek Naug Hewlett Packard Enterprise
  • Antonio Guillen Hewlett Packard Enterprise
  • Ricardo Luna Hewlett Packard Enterprise
  • Vineet Gundecha Hewlett Packard Enterprise
  • Ashwin Ramesh Babu Hewlett Packard Enterprise
  • Sajad Mousavi Hewlett Packard Enterprise



Simulation environments for AI agents and multi-agent systems, Environment-aware agent behaviors, Intelligent collaborative systems, Artificial Intelligence


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:




How to Cite

Sarkar, S., Naug, A., Guillen, A., Luna, R., Gundecha, V., Ramesh Babu, A., & Mousavi, S. (2024). Sustainability of Data Center Digital Twins with Reinforcement Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23832-23834.