Summary of Carbon Footprint Reduction For Sustainable Data Centers in Real-time, by Soumyendu Sarkar et al.
Carbon Footprint Reduction for Sustainable Data Centers in Real-Time
by Soumyendu Sarkar, Avisek Naug, Ricardo Luna, Antonio Guillen, Vineet Gundecha, Sahand Ghorbanpour, Sajad Mousavi, Dejan Markovikj, Ashwin Ramesh Babu
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); 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 The proposed Data Center Carbon Footprint Reduction (DC-CFR) multi-agent Reinforcement Learning (MARL) framework optimizes data centers for the multiple objectives of carbon footprint reduction, energy consumption, and energy cost. The framework leverages collaborative agents to optimize power consumption in cooling and IT loads, shifting flexible loads based on renewable energy availability, and utilizing battery storage from uninterrupted power supply. The results show that DC-CFR MARL agents effectively resolve complex interdependencies in optimizing cooling, load shifting, and energy storage in real-time for various locations under dynamic weather and grid carbon intensity conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to make data centers more sustainable by using artificial intelligence to optimize how they use power. Right now, data centers use a lot of energy, which is bad for the environment. The goal is to find ways to reduce this energy consumption while also keeping the data center running smoothly. The researchers created an AI system that can do this by adjusting things like cooling systems and battery storage in real-time based on factors like weather and the amount of renewable energy available. |
Keywords
* Artificial intelligence * Reinforcement learning
