WaniaKhance/Real-Time_Data_Center_Energy_Management

This project is Master thesis research conducted at ENEA Portici Research Center, Italy. The data is obtained from the HPC CRESCO6 cluster at ENEA Portici Research Center. The aim is to identify energy consuming areas within the data center. In this project, real-time dataset from ENEA Portici Research Center is used. There are several techniques implemented including big data analytics and AI technology.

20
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Experimental

This project helps data center managers understand and predict energy consumption and thermal conditions. It takes real-time data from server sensors, job logs, cooling systems, and environmental sensors to identify areas of energy waste and forecast future resource utilization. The goal is to optimize energy efficiency within the data center environment.

No commits in the last 6 months.

Use this if you manage a data center and need to proactively identify energy-consuming areas, predict resource usage, and classify IT room thermal conditions to improve energy efficiency.

Not ideal if you are looking for a plug-and-play solution that does not require adaptation to your specific data center's sensor and job data.

data-center-management energy-efficiency resource-forecasting thermal-management operations-optimization
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 4 / 25
Maturity 16 / 25
Community 0 / 25

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8

Forks

Language

Jupyter Notebook

License

GPL-3.0

Last pushed

Aug 30, 2022

Commits (30d)

0

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