Towards Extension of Data Centre Modelling Toolbox with Parameters Estimation

• Main Authors: Berezovskaya, Yulia, Yang, Chen Wei, Vyatkin, Valeriy
• Other Authors: Camarinha-Matos, Luis M., Ferreira, Pedro, Brito, Guilherme, Luleå University of Technology, Information Technologies in Industrial Automation, Department of Electrical Engineering and Automation, Aalto-yliopisto, Aalto University
• Format: Other/Unknown Material
• Language: English
• Published: 2021
• Subjects: Data centre; Matlab/Simulink; Modelling; Parameter estimation; Power consumption; Luleå
• Online Access: https://aaltodoc.aalto.fi/handle/123456789/111395; https://doi.org/10.1007/978-3-030-78288-7_18

Funding Information: This project has been funded by partners of the ERA-Net SES 2018 joint call RegSys (www.eranet-smartenergysystems.eu) ? a network of 30 national and regional RTD funding agencies of 23 European countries. As such, this project has received funding from the European Union?s Horizon 2020 research and innovation programme under grant agreement no. 775970. Publisher Copyright: © 2021, IFIP International Federation for Information Processing. Modern data centres consume a significant amount of electricity. Therefore, they require techniques for improving energy efficiency and reducing energy waste. The promising energy-saving methods are those, which adapt the system energy use based on resource requirements at run-time. These techniques require testing their performance, reliability and effect on power consumption in data centres. Generally, real data centres cannot be used as a test site because of such experiments may violate safety and security protocols. Therefore, examining the performance of different energy-saving strategies requires a model, which can replace the real data centre. The model is expected to accurately estimate the energy consumption of data centre components depending on their utilisation. This work presents a toolbox for data centre modelling. The toolbox is a set of building blocks representing individual components of a typical data centre. The paper concentrates on parameter estimation methods, which use data, collectedfrom a real data centre and adjust parameters of building blocks so that the model represents the data centre most accurately. The paper also demonstrates the results of parameters estimation on an example of EDGE module of SICS ICE data centre located in Luleå, Sweden. Peer reviewed