Data Center e Eficiência Energética

Data Center and Energy Efficiency

Autores

  • José Luiz Romero de Brito
  • Patrícia Helena lara dos Santos Matai
  • Mario Roberto dos Santos

DOI:

https://doi.org/10.34140/bjbv5n2-002

Palavras-chave:

Eficiência energética, Data center, Consumo de energia, Tecnologia da informação

Resumo

O rápido desenvolvimento da internet exige que as infraestruturas de data centers sejam desenvolvidas em grande escala com maior capacidade de energia e também alcancem formas para a redução no consumo dessa energia e também nas emissões de carbono. O objetivo da pesquisa foi verificar na literatura como os pesquisadores estão avaliando a eficiência energética em data centers e quais são as soluções para a melhoria dessa eficiência. Para tanto foi realizada uma pesquisa da base de dados ScienceDirect utilizando as palavras “energy efficiency and data center” sendo encontrados 75 artigos. Os principais resultados dos artigos foram: redução do uso do ventilador do servidor; uso dos sistemas de refrigeração e ventilação; barramentos CC supercondutores de alta temperatura; algoritmo para minimizar o alto consumo de energia; algoritmo dinâmico de agendamento de tarefas online; modelo de consumo de energia dinâmico acoplado incluindo dispositivos de TI e de refrigeração; melhorar a distribuição do fluxo de ar; sistema combinado de resfriamento, aquecimento e energia; sistema de resfriamento de tubo de calor integrado; uso de energias renováveis; desligando e ligando portas da rede.  

Referências

AN, H.; MA, X. Dynamic coupling real-time energy consumption modeling for data centers. Energy Reports, v. 8, sup. 10, p. 1184-1192, 2022. https://doi.org/10.1016/j.egyr.2022.06.085.

ARSHAD, U. et al. Utilizing power consumption and SLA violations using dynamic VM consolidation in cloud data centers. Renewable and Sustainable Energy Reviews, v. 167, 112782, p. 1-14, 2022. https://doi.org/10.1016/j.rser.2022.112782.

BARDIN, L. Análise de conteúdo, (5a ed.). Lisboa: Edições 70. Lda, 2009.

BASHROUSH, R. et al. Optimizing server refresh cycles: the case for circular economy with an aging Moore’s Law. IEEE Transactions on Sustainable Computing, v. 7, n.1, p.189-200, 2022. https://doi.org/10.1109/TSUSC.2020.3035234

BRADY, G. A. et al. A case study and critical assessment in calculating power usage effectiveness for a data centre. Energy Conversion and Management, v. 76, p. 155-161, 2013. https://doi.org/10.1016/j.enconman.2013.07.035.

CAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR. (2022). Plataforma Sucupira. Disponível em: <https://sucupira.capes.gov.br/sucupira/public/consultas/coleta/veiculoPublicacaoQualis/listaConsultaGeralPeriodicos.jsf>. Acesso em: 16 Ago. 2022.

CHEN, X. et al. A 10 MW class data center with ultra-dense high-efficiency energy distribution: Design and economic evaluation of superconducting DC busbar networks. Energy, v. 250, 123820, p. 1-17, 2022. https://doi.org/10.1016/j.energy.2022.123820.

CHKIRBENE, Z. et al. Efficient techniques for energy saving in data center networks. Computer Communications, v. 129, p. 111-124, 2018. https://doi.org/10.1016/j.comcom.2018.07.025.

CHOI, Y. J. et al. Development of an adaptive artificial neural network model and optimal control algorithm for a data center cyber–physical system. Building and Environment, v. 210, 108704, p. 1-17, 2022. https://doi.org/10.1016/j.buildenv.2021.108704.

DWIVEDI, Y. K. et al. Metaverse beyond the hype: multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, v. 66, 102542, p. 1-55, 2022. https://doi.org/10.1016/j.ijinfomgt.2022.102542.

GUO, C. et al. Energy management of internet data centers in multiple local energy markets. Electric Power Systems Research, v. 205, 107760, p. 1-12, 2022. https://doi.org/10.1016/j.epsr.2021.107760.

JIN, C.; BAI, X.; YANG, C. Effects of airflow on the thermal environment and energy efficiency in raised-floor data centers: A review. Science of The Total Environment, v. 695, 133801, p. 1-17, 2019. https://doi.org/10.1016/j.scitotenv.2019.133801.

KUZAY, M. et al. Retrofitting of an air-cooled data center for energy efficiency. Case Studies in Thermal Engineering, v. 36, 102228, p. 1-14, 2022. https://doi.org/10.1016/j.csite.2022.102228.

LI, X. et al. Energy-aware cloud workflow applications scheduling with geo-distributed data. IEEE Transactions on Services Computing, v. 15, n. 2, p. 891-903, 2022. https://doi.org/10.1109/TSC.2020.2965106.

LIU, Y. et al. Energy consumption and emission mitigation prediction based on data center traffic and PUE for global data center. Global Energy Interconnection, v. 3, n. 3, p. 272-282, 2020. https://doi.org/10.1016/j.gloei.2020.07.008.

LONG, S. et al. A review of energy efficiency evaluation technologies in cloud data centers. Energy and Buildings, v. 260, 111848, p. 1-14, 2022. https://doi.org/10.1016/j.enbuild.2022.111848.

MA, H.; DING, A. Method for evaluation on energy consumption of cloud computing data center based on deep reinforcement learning. Electric Power Systems Research, v. 208, 107899, p. 1-7, 2022. https://doi.org/10.1016/j.epsr.2022.107899.

MAHBOD, M. H. B. et al. Energy saving evaluation of an energy efficient data center using a model-free reinforcement learning approach. Applied Energy, v. 322, 119392, p. 1-14, 2022. https://doi.org/10.1016/j.apenergy.2022.119392.

MALONE, C.; BELADY, C. Metrics to characterize data center & IT equipment energy use. In: Proceedings of the Digital Power Forum. Richardson, TX, USA; 2006.

MASANET, E. et al. Recalibrating global data center energy-use estimates. Science, v. 367, n. 6481, p. 984-986, 2020. https://doi.org/10.1126/science.aba3758.

MATERWALA, H.; ISMAIL, L. Performance and energy-aware bi-objective tasks scheduling for cloud data centers. Procedia Computer Science, v. 197, p. 238-246, 2022. https://doi.org/10.1016/j.procs.2021.12.137.

PENG, X. et al. Exploiting renewable energy and UPS systems to reduce power consumption in data centers. Big Data Research, v. 27, 100306, p. 1-12, 2022. https://doi.org/10.1016/j.bdr.2021.100306.

REDDY, K. H. K. et al. Towards energy efficient Smart city services: A software defined resource management scheme for data centers. Sustainable Computing: Informatics and Systems, v. 35, 100776, p. 1-9, 2022. https://doi.org/10.1016/j.suscom.2022.100776.

RTEIL, N. et al. Interact: IT infrastructure energy and cost analyzer tool for data centers. Sustainable Computing: Informatics and Systems, v. 33, 100618, p. 1-10, 2022. https://doi.org/10.1016/j.suscom.2021.100618.

SANTOS, M. R.; BRITO, J. L. R; SHIBAO, F. Y. Academic approach on solar photovoltaic energy in Brazil. Scientific Journal of Applied Social and Clinical Science, v. 2, n. 21, p 1-12, 2022. https://doi.org/10.22533/at.ed.2162212220104.

SHAO, X. et al. A review of energy efficiency evaluation metrics for data centers. Energy and Buildings, v. 271, 112308, p. 1-19, 2022. https://doi.org/10.1016/j.enbuild.2022.112308.

SYNERGY RESEARCH GROUP. Pipeline of over 300 new hyperscale Data Centers drives healthy growth forecasts. Reno, NV, March 23: 2022. Disponível em:< Pipeline of Over 300 New Hyperscale Data Centers Drives Healthy Growth Forecasts | Synergy Research Group (srgresearch.com)>. Acesso em: 16 Ago. 2022.

SYNERGY RESEARCH GROUP. Hyperscale Data Center capacity doubles in under four years; the US still accounts for half. Reno, NV, November 17: 2021 Disponível em: <https://www.srgresearch.com/articles/as-hyperscale-data-center-capacity-doubles-in-under-four-years-the-us-still-accounts-for-half-of-the-total>. Acesso em: 16 Ago. 2022.

THE GREEN GRID. Green grid metrics: describing datacenter power efficiency. 2007. Disponível em: <https://leonardo-energy.pl/wp-content/uploads/2018/03/Green_Grid_Metrics.pdf>. Acesso em: 20 Out. 2022.

TIAN, Q. et al. Robust optimal energy management of data center equipped with multi-energy conversion technologies. Journal of Cleaner Production, v. 329, 129616, p. 1-13, 2021. https://doi.org/10.1016/j.jclepro.2021.129616.

TRANFIELD, D.; DENYER, D.; SMART, P. Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management, v. 14, n. 3, p. 207-222, 2003. https://doi.org/10.1111/14678551.00375.

WANG, Y. et al. Towards an energy-efficient Data Center Network based on deep reinforcement learning. Computer Networks, v. 210, 108939, p. 1-10, 2022. https://doi.org/10.1016/j.comnet.2022.108939.

WEBSTER, J.; WATSON, R. T. Analyzing the past to prepare for de future: writing a literature review. MIS Quarterly, v. 26, n. 2, p. 13-23, 2002.

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Publicado

2023-04-16

Como Citar

de Brito, J. L. R., Matai, P. H. lara dos S., & dos Santos, M. R. (2023). Data Center e Eficiência Energética: Data Center and Energy Efficiency. Brazilian Journal of Business, 5(2), 786–795. https://doi.org/10.34140/bjbv5n2-002

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