Towards Extension of Data Centre Modelling Toolbox with Parameters Estimation

Yulia Berezovskaya; Chen Wei Yang; Valeriy Vyatkin

doi:10.1007/978-3-030-78288-7_18

Towards Extension of Data Centre Modelling Toolbox with Parameters Estimation

Yulia Berezovskaya^*
, Chen Wei Yang
, Valeriy Vyatkin

^*Corresponding author for this work

Luleå University of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

1 Citation (Scopus)

48 Downloads (Pure)

Abstract

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, collected from 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.

Original language	English
Title of host publication	Technological Innovation for Applied AI Systems
Subtitle of host publication	Proceedings of 12th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2021
Editors	Luis M. Camarinha-Matos, Pedro Ferreira, Guilherme Brito
Publisher	Springer
Pages	189-196
Number of pages	8
ISBN (Electronic)	9783030782887
ISBN (Print)	9783030782870
DOIs	https://doi.org/10.1007/978-3-030-78288-7_18
Publication status	Published - Jul 2021
MoE publication type	A4 Conference publication
Event	Advanced Doctoral Conference on Computing, Electrical and Industrial Systems - Virtual, Online, Caparica, Portugal Duration: 7 Jul 2021 → 9 Jul 2021 Conference number: 12 https://doceis.dee.fct.unl.pt/

Publication series

Name	IFIP Advances in Information and Communication Technology
Volume	626
ISSN (Print)	1868-4238
ISSN (Electronic)	1868-422X

Conference

Conference	Advanced Doctoral Conference on Computing, Electrical and Industrial Systems
Abbreviated title	DoCEIS
Country/Territory	Portugal
City	Caparica
Period	07/07/2021 → 09/07/2021
Internet address	https://doceis.dee.fct.unl.pt/

Funding

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.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy
SDG 12 Responsible Consumption and Production

Keywords

Data centre
Matlab/Simulink
Modelling
Parameter estimation
Power consumption

Access to Document

10.1007/978-3-030-78288-7_18

ELEC_Berezovskaya_etal_Towards_Extension_of_Data_Centre_Modelling_DoCEIS_2021Accepted author manuscript, 415 KB

Cite this

Berezovskaya, Y., Yang, C. W., & Vyatkin, V. (2021). Towards Extension of Data Centre Modelling Toolbox with Parameters Estimation. In L. M. Camarinha-Matos, P. Ferreira, & G. Brito (Eds.), Technological Innovation for Applied AI Systems : Proceedings of 12th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2021 (pp. 189-196). (IFIP Advances in Information and Communication Technology; Vol. 626). Springer. https://doi.org/10.1007/978-3-030-78288-7_18

Berezovskaya, Yulia ; Yang, Chen Wei ; Vyatkin, Valeriy. / Towards Extension of Data Centre Modelling Toolbox with Parameters Estimation. Technological Innovation for Applied AI Systems : Proceedings of 12th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2021. editor / Luis M. Camarinha-Matos ; Pedro Ferreira ; Guilherme Brito. Springer, 2021. pp. 189-196 (IFIP Advances in Information and Communication Technology).

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title = "Towards Extension of Data Centre Modelling Toolbox with Parameters Estimation",

abstract = "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, collected from 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 Lule{\aa}, Sweden.",

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note = "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: {\textcopyright} 2021, IFIP International Federation for Information Processing.; Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS ; Conference date: 07-07-2021 Through 09-07-2021",

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Berezovskaya, Y, Yang, CW & Vyatkin, V 2021, Towards Extension of Data Centre Modelling Toolbox with Parameters Estimation. in LM Camarinha-Matos, P Ferreira & G Brito (eds), Technological Innovation for Applied AI Systems : Proceedings of 12th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2021. IFIP Advances in Information and Communication Technology, vol. 626, Springer, pp. 189-196, Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, Caparica, Portugal, 07/07/2021. https://doi.org/10.1007/978-3-030-78288-7_18

Towards Extension of Data Centre Modelling Toolbox with Parameters Estimation. / Berezovskaya, Yulia; Yang, Chen Wei; Vyatkin, Valeriy.
Technological Innovation for Applied AI Systems : Proceedings of 12th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2021. ed. / Luis M. Camarinha-Matos; Pedro Ferreira; Guilherme Brito. Springer, 2021. p. 189-196 (IFIP Advances in Information and Communication Technology; Vol. 626).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Towards Extension of Data Centre Modelling Toolbox with Parameters Estimation

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AU - Yang, Chen Wei

AU - Vyatkin, Valeriy

N1 - Conference code: 12

PY - 2021/7

Y1 - 2021/7

N2 - 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, collected from 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.

AB - 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, collected from 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.

KW - Data centre

KW - Matlab/Simulink

KW - Modelling

KW - Parameter estimation

KW - Power consumption

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DO - 10.1007/978-3-030-78288-7_18

M3 - Conference article in proceedings

AN - SCOPUS:85112024294

SN - 9783030782870

T3 - IFIP Advances in Information and Communication Technology

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EP - 196

BT - Technological Innovation for Applied AI Systems

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A2 - Brito, Guilherme

PB - Springer

T2 - Advanced Doctoral Conference on Computing, Electrical and Industrial Systems

Y2 - 7 July 2021 through 9 July 2021

ER -

Berezovskaya Y, Yang CW, Vyatkin V. Towards Extension of Data Centre Modelling Toolbox with Parameters Estimation. In Camarinha-Matos LM, Ferreira P, Brito G, editors, Technological Innovation for Applied AI Systems : Proceedings of 12th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2021. Springer. 2021. p. 189-196. (IFIP Advances in Information and Communication Technology). doi: 10.1007/978-3-030-78288-7_18

Towards Extension of Data Centre Modelling Toolbox with Parameters Estimation

Abstract

Publication series

Conference

Funding

UN SDGs

Keywords

Access to Document

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Cite this