{
  "id": "2310.03906",
  "version": "v1",
  "published": "2023-10-05T21:24:54.000Z",
  "updated": "2023-10-05T21:24:54.000Z",
  "title": "PyDCM: Custom Data Center Models with Reinforcement Learning for Sustainability",
  "authors": [
    "Avisek Naug",
    "Antonio Guillen",
    "Ricardo Luna Gutiérrez",
    "Vineet Gundecha",
    "Dejan Markovikj",
    "Lekhapriya Dheeraj Kashyap",
    "Lorenz Krause",
    "Sahand Ghorbanpour",
    "Sajad Mousavi",
    "Ashwin Ramesh Babu",
    "Soumyendu Sarkar"
  ],
  "comment": "The 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys '23), November 15--16, 2023, Istanbul, Turkey",
  "doi": "10.1145/3600100.3623732",
  "categories": [
    "cs.LG"
  ],
  "abstract": "The increasing global emphasis on sustainability and reducing carbon emissions is pushing governments and corporations to rethink their approach to data center design and operation. Given their high energy consumption and exponentially large computational workloads, data centers are prime candidates for optimizing power consumption, especially in areas such as cooling and IT energy usage. A significant challenge in this pursuit is the lack of a configurable and scalable thermal data center model that offers an end-to-end pipeline. Data centers consist of multiple IT components whose geometric configuration and heat dissipation make thermal modeling difficult. This paper presents PyDCM, a customizable Data Center Model implemented in Python, that allows users to create unique configurations of IT equipment with custom server specifications and geometric arrangements of IT cabinets. The use of vectorized thermal calculations makes PyDCM orders of magnitude faster (30 times) than current Energy Plus modeling implementations and scales sublinearly with the number of CPUs. Also, PyDCM enables the use of Deep Reinforcement Learning via the Gymnasium wrapper to optimize data center cooling and offers a user-friendly platform for testing various data center design prototypes.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-10-05T21:24:54.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "custom data center models",
      "reinforcement learning",
      "thermal data center model",
      "data center design",
      "sustainability"
    ],
    "tags": [
      "conference paper",
      "journal article"
    ],
    "publication": {
      "publisher": "ACM",
      "journal": "Commun. ACM"
    },
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}