Automation of Thermal Processes in a Datacenter. Experimental Control Using Cloud Based Platform and PLC
The primary goal of this project is to enhance the cooling system of the Data Center at INCDTIM Cluj-Napoca to boost computing capacity, without having to invest in more efficient cooling equipment or relocate to a larger facility. The project began with the creation of a 3D model of the Data Center, followed by simulating the existing conditions using Ansys CFD (Computational Fluid Dynamics). After identifying the optimal solutions and improvements, these were simulated and validated with Ansys CFD. The construction of the new pipe system then proceeded. This passive system was designed to improve airflow within the Data Center, and its performance matched the simulated predictions. Next, automation systems were developed to control and monitor thermal processes, with data being recorded in the Cloud. The automation will be managed via a PLC, and integrating the PLC with a Cloud platform allows for remote monitoring and control of thermal processes. A Cloud-based application will be utilized for remote system monitoring. This interface enables technicians to view all temperature readings and system settings, providing real-time visibility into the Data Center's thermal processes. Additionally, collected data will facilitate the development of an experimental machine learning application. This application will analyze historical temperature fluctuations to predict and optimize settings, ensuring consistent temperatures throughout the room even when servers generate heat inconsistently.
Authors
- Jefte Nagy
- Cristian-Andrei Lupşe
- F. Fărcaş (ORCID: https://orcid.org/0000-0002-8572-1223)
- Mihail-Radu-Cătălin Truşcă
- Victor Zârbo
Institutions
- National Institute for Research and Development of Isotopic and Molecular Technologies (RO)
Publication Details
- Published
- 2024-10-23
- DOI
- https://doi.org/10.1109/icasc63229.2024.10784622
- Primary Topic
- Cloud Computing and Resource Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00