A Configurable Pythonic Data Center Model for Sustainable Cooling and ML Integration

• URL: http://arxiv.org/abs/2404.12498v1
• Date: Thu, 18 Apr 2024 20:25:33 GMT
• Title: A Configurable Pythonic Data Center Model for Sustainable Cooling and ML Integration
• Authors: Avisek Naug, Antonio Guillen, Ricardo Luna Gutierrez, Vineet Gundecha, Sahand Ghorbanpour, Sajad Mousavi, Ashwin Ramesh Babu, Soumyendu Sarkar,
• Abstract summary: We showcase PyDCM, a Python library that enables extremely fast prototyping of data center design. We demonstrate capabilities of PyDCM and compare them to existing works in EnergyPlus for modeling data centers. PyDCM can also be used as a standalone Gymnasium environment for demonstrating sustainability-focused data center control.
• Score: 4.0196072781228285
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There have been growing discussions on estimating and subsequently reducing the operational carbon footprint of enterprise data centers. The design and intelligent control for data centers have an important impact on data center carbon footprint. In this paper, we showcase PyDCM, a Python library that enables extremely fast prototyping of data center design and applies reinforcement learning-enabled control with the purpose of evaluating key sustainability metrics including carbon footprint, energy consumption, and observing temperature hotspots. We demonstrate these capabilities of PyDCM and compare them to existing works in EnergyPlus for modeling data centers. PyDCM can also be used as a standalone Gymnasium environment for demonstrating sustainability-focused data center control.

Related papers

• Sustainability of Data Center Digital Twins with Reinforcement Learning [2.4971633082970377]
  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. DCRL-Green is 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.
  arXiv  Detail & Related papers  (2024-04-16T18:22:30Z)
• IoTCO2: Assessing the End-To-End Carbon Footprint of Internet-of-Things-Enabled Deep Learning [6.1356834243468565]
  Deep learning (DL) models are increasingly deployed on Internet of Things (IoT) devices for data processing. Existing carbon footprint modeling tools neglect non-computing hardware components common in IoT devices. This paper introduces textitcarb, an end-to-end modeling tool for precise carbon footprint estimation in IoT-enabled DL.
  arXiv  Detail & Related papers  (2024-03-16T17:32:59Z)
• PyDCM: Custom Data Center Models with Reinforcement Learning for Sustainability [2.6429542504022314]
  PyDCM is a customizable Data Center Model implemented in Python. The use of vectorized thermal calculations makes PyDCM orders of magnitude faster (30 times) than current Energy Plus modeling implementations.
  arXiv  Detail & Related papers  (2023-10-05T21:24:54Z)
• SHIELD: Sustainable Hybrid Evolutionary Learning Framework for Carbon, Wastewater, and Energy-Aware Data Center Management [2.9699290794642366]
  Geo-distributed data centers (GDDCs) have a significant associated environmental impact. This paper proposes a novel framework to co-optimize carbon emissions, water footprint, and energy costs of GDDCs.
  arXiv  Detail & Related papers  (2023-08-24T21:11:55Z)
• DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systems [81.21462458089142]
  Data-centric AI is emerging as a unifying paradigm that could enable reliable end-to-end pipelines. We propose DC-Check, an actionable checklist-style framework to elicit data-centric considerations. This data-centric lens on development aims to promote thoughtfulness and transparency prior to system development.
  arXiv  Detail & Related papers  (2022-11-09T17:32:09Z)
• Measuring the Carbon Intensity of AI in Cloud Instances [91.28501520271972]
  We provide a framework for measuring software carbon intensity, and propose to measure operational carbon emissions. We evaluate a suite of approaches for reducing emissions on the Microsoft Azure cloud compute platform.
  arXiv  Detail & Related papers  (2022-06-10T17:04:04Z)
• Treehouse: A Case For Carbon-Aware Datacenter Software [4.7521372297013365]
  The end of Dennard scaling and the slowing of Moore's Law has put the energy use of datacenters on an unsustainable path. We argue that substantial reductions in the carbon intensity of datacenter computing are possible with a software-centric approach.
  arXiv  Detail & Related papers  (2022-01-06T16:00:53Z)
• Power Modeling for Effective Datacenter Planning and Compute Management [53.41102502425513]
  We discuss two classes of statistical power models designed and validated to be accurate, simple, interpretable and applicable to all hardware configurations and workloads. We demonstrate that the proposed statistical modeling techniques, while simple and scalable, predict power with less than 5% Mean Absolute Percent Error (MAPE) for more than 95% diverse Power Distribution Units (more than 2000) using only 4 features.
  arXiv  Detail & Related papers  (2021-03-22T21:22:51Z)
• A Framework for Energy and Carbon Footprint Analysis of Distributed and Federated Edge Learning [48.63610479916003]
  This article breaks down and analyzes the main factors that influence the environmental footprint of distributed learning policies. It models both vanilla and decentralized FL policies driven by consensus. Results show that FL allows remarkable end-to-end energy savings (30%-40%) for wireless systems characterized by low bit/Joule efficiency.
  arXiv  Detail & Related papers  (2021-03-18T16:04:42Z)
• PyHealth: A Python Library for Health Predictive Models [53.848478115284195]
  PyHealth is an open-source Python toolbox for developing various predictive models on healthcare data. The data preprocessing module enables the transformation of complex healthcare datasets into machine learning friendly formats. The predictive modeling module provides more than 30 machine learning models, including established ensemble trees and deep neural network-based approaches.
  arXiv  Detail & Related papers  (2021-01-11T22:02:08Z)
• Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning [68.37641996188133]
  We introduce a framework for tracking realtime energy consumption and carbon emissions. We create a leaderboard for energy efficient reinforcement learning algorithms. We propose strategies for mitigation of carbon emissions and reduction of energy consumption.
  arXiv  Detail & Related papers  (2020-01-31T05:12:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.