Summary of A Configurable Pythonic Data Center Model For Sustainable Cooling and Ml Integration, by Avisek Naug et al.
A Configurable Pythonic Data Center Model for Sustainable Cooling and ML Integration
by Avisek Naug, Antonio Guillen, Ricardo Luna Gutierrez, Vineet Gundecha, Sahand Ghorbanpour, Sajad Mousavi, Ashwin Ramesh Babu, Soumyendu Sarkar
First submitted to arxiv on: 18 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper introduces PyDCM, a Python library designed for rapid prototyping of data center design and intelligent control. The library leverages reinforcement learning to evaluate key sustainability metrics such as carbon footprint, energy consumption, and temperature hotspots. By showcasing the capabilities of PyDCM, the authors demonstrate its potential in reducing the operational carbon footprint of enterprise data centers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special tool called PyDCM that helps design and control big data centers so they use less energy and reduce their carbon footprint. The tool uses machine learning to test different designs and see how well they work. This can help make data centers more sustainable and environmentally friendly. |
Keywords
* Artificial intelligence * Machine learning * Reinforcement learning * Temperature
