Análise de Zonas Térmicas em Data Center Não-CRAC
Resumo
A elevada concentração de equipamentos em Data Centers (DCs) é objeto de estudo para administradores, fabricantes (processadores, servidores e sistemas de refrigeração), entre outros. Dentre os desafios da área, destaca-se o melhoramento da eficiência energética destes ambientes. No contexto de DC, a Power Usage Effectiveness (PUE) é uma referência na mensuração da eficiência energética. Este trabalho apresenta a arquitetura MonTerDC, um sistema de monitoração de temperatura de DC baseado em sistemas de refrigeração não-CRAC. Como resultados, o trabalha apresenta o uso do MonTerDC na identificação de zonas térmicas indesejáveis (fora da norma). Com este mapeamento térmico em zonas, o administrador do DC pode aplicar práticas a` melhor distribuição física dos nós de computação e, consequentemente, reduzir a temperatura das zonas térmicas.
Referências
Ahuja, N., Rego, C., Ahuja, S., Warner, M., and Docca, A. (2011). Data center efciency with higher ambient temperatures and optimized cooling control. In 2011 27th Annual IEEE Semiconductor Thermal Measurement and Management Symposium, pages 105– 109.
Alkharabsheh, S., Sammakia, B., Shrivastava, S., and Schmidt, R. (2014). Implementing rack thermal capacity in a room level CFD model of a data center. In 2014 Semiconductor Thermal Measurement and Management Symposium (SEMI-THERM), pages 188–192.
Arghode, V. K. and Joshi, Y. (2013). Modeling Strategies for Air Flow Through Perforated Tiles in a Data Center. IEEE Transactions on Components, Packaging and Manufacturing Technology, 3(5):800–810.
ASHRAE (2016). Ashrae tc9.9, data center networking equipment – issues and best practices.
Athavale, J., Joshi, Y., Yoda, M., and Phelps, W. (2016). Impact of active tiles on data center ow and temperature distribution. In 2016 15th IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (ITherm), pages 1162–1171.
Avelar, V., Azevedo, D., and French, A. (2012). PUETM: A Comprehensive examination of the metric.
Bottari, G. D. (2014). Monitoramento Térmico Responsivo para Centros de Processamento de Dados. Master Degree, Universidade Federal Fluminense, Niteroi/RJ Brasil.
Facebook (2017). Lulea datacenter. In https://www.facebook.com/LuleaDataCenter.
Fulpagare, Y., Shirbhate, P., and Bhargav, A. (2016). Design and testing of prototype data center. In 15th IEEE ITHERM Conference, Indian Institute of Technology Gandhinagar.
Gao, T., Kumar, E., Sahini, M., Ingalz, C., Heydari, A., Lu, W., and Sun, X. (2016). Innovative server rack design with bottom located cooling unit. In 2016 15th IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (ITherm), pages 1172–1181.
Google (2012). Google datacenters from paper mill to data center. In https://www.google.com/about/datacenters/inside/locations/hamina/.
Horner, N. and Azevedo, I. (2016). Power usage effectiveness in data centers: Overloaded and underachieving. In The Electricity Journal 29, pages 61–69.
Lei, L., Liang, C., and Liu, J. (2011). Thermocast: A cyber-physical forecasting model In 17th ACM SIGKDD Conference on Knowledge Discovery, San for data centers. Diego, California, USA.
Marshall, L. and Bemis, P. (2011). Using CFD for data center design and analysis. Applied Math Modeling White Paper.
Pakbaznia, E., Ghasemazar, M., and Pedram, M. (2010). Temperature-aware dynamic resource provisioning in a power-optimized datacenter. In 2010 Design, Automation Test in Europe Conference Exhibition (DATE 2010), pages 124–129.
Schneider, E. (2014). Soluções em climatização para data center. Brasília/Brasil. XIV Encontro Nacional de Empresas Projetistas e Consultores da Abrava.
Song, Z., Zhang, X., and Eriksson, C. (2015). Data Center Energy and Cost Saving Evaluation. Energy Procedia, 75:1255–1260.
Sverdlik, Y. (2014). Survey: Industry average data center pue stays nearly at over four years. Uptime Institute.
Tang, Q., Gupta, S. K. S., and Varsamopoulos, G. (2008). Energy-Efcient ThermalAware Task Scheduling for Homogeneous High-Performance Computing Data CenIEEE Transactions on Parallel and Distributed ters: A Cyber-Physical Approach. Systems, 19(11):1458–1472.
Wibron, E. (2015). CFD Modeling of an Air-Cooled Data Center. Master Degree, CHALMERS University of Technology, Gothenburg/Sweden.
Zhang, S., Liu, X., Ahuja, N., Han, Y., Liu, L., Liu, S., and Shen, Y. (2015). On demand cooling with real time thermal information. In 2015 31st Thermal Measurement, Modeling Management Symposium (SEMI-THERM), pages 138–146.
Alkharabsheh, S., Sammakia, B., Shrivastava, S., and Schmidt, R. (2014). Implementing rack thermal capacity in a room level CFD model of a data center. In 2014 Semiconductor Thermal Measurement and Management Symposium (SEMI-THERM), pages 188–192.
Arghode, V. K. and Joshi, Y. (2013). Modeling Strategies for Air Flow Through Perforated Tiles in a Data Center. IEEE Transactions on Components, Packaging and Manufacturing Technology, 3(5):800–810.
ASHRAE (2016). Ashrae tc9.9, data center networking equipment – issues and best practices.
Athavale, J., Joshi, Y., Yoda, M., and Phelps, W. (2016). Impact of active tiles on data center ow and temperature distribution. In 2016 15th IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (ITherm), pages 1162–1171.
Avelar, V., Azevedo, D., and French, A. (2012). PUETM: A Comprehensive examination of the metric.
Bottari, G. D. (2014). Monitoramento Térmico Responsivo para Centros de Processamento de Dados. Master Degree, Universidade Federal Fluminense, Niteroi/RJ Brasil.
Facebook (2017). Lulea datacenter. In https://www.facebook.com/LuleaDataCenter.
Fulpagare, Y., Shirbhate, P., and Bhargav, A. (2016). Design and testing of prototype data center. In 15th IEEE ITHERM Conference, Indian Institute of Technology Gandhinagar.
Gao, T., Kumar, E., Sahini, M., Ingalz, C., Heydari, A., Lu, W., and Sun, X. (2016). Innovative server rack design with bottom located cooling unit. In 2016 15th IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (ITherm), pages 1172–1181.
Google (2012). Google datacenters from paper mill to data center. In https://www.google.com/about/datacenters/inside/locations/hamina/.
Horner, N. and Azevedo, I. (2016). Power usage effectiveness in data centers: Overloaded and underachieving. In The Electricity Journal 29, pages 61–69.
Lei, L., Liang, C., and Liu, J. (2011). Thermocast: A cyber-physical forecasting model In 17th ACM SIGKDD Conference on Knowledge Discovery, San for data centers. Diego, California, USA.
Marshall, L. and Bemis, P. (2011). Using CFD for data center design and analysis. Applied Math Modeling White Paper.
Pakbaznia, E., Ghasemazar, M., and Pedram, M. (2010). Temperature-aware dynamic resource provisioning in a power-optimized datacenter. In 2010 Design, Automation Test in Europe Conference Exhibition (DATE 2010), pages 124–129.
Schneider, E. (2014). Soluções em climatização para data center. Brasília/Brasil. XIV Encontro Nacional de Empresas Projetistas e Consultores da Abrava.
Song, Z., Zhang, X., and Eriksson, C. (2015). Data Center Energy and Cost Saving Evaluation. Energy Procedia, 75:1255–1260.
Sverdlik, Y. (2014). Survey: Industry average data center pue stays nearly at over four years. Uptime Institute.
Tang, Q., Gupta, S. K. S., and Varsamopoulos, G. (2008). Energy-Efcient ThermalAware Task Scheduling for Homogeneous High-Performance Computing Data CenIEEE Transactions on Parallel and Distributed ters: A Cyber-Physical Approach. Systems, 19(11):1458–1472.
Wibron, E. (2015). CFD Modeling of an Air-Cooled Data Center. Master Degree, CHALMERS University of Technology, Gothenburg/Sweden.
Zhang, S., Liu, X., Ahuja, N., Han, Y., Liu, L., Liu, S., and Shen, Y. (2015). On demand cooling with real time thermal information. In 2015 31st Thermal Measurement, Modeling Management Symposium (SEMI-THERM), pages 138–146.
Publicado
17/10/2017
Como Citar
CAMILLO JUNIOR, Ademir; C. MIERS, Charles; P. KOSLOVSKI, Guilherme; A. PILLON, Mauricio.
Análise de Zonas Térmicas em Data Center Não-CRAC. In: SIMPÓSIO EM SISTEMAS COMPUTACIONAIS DE ALTO DESEMPENHO (SSCAD), 18. , 2017, Campinas.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2017
.
p. 124-135.
DOI: https://doi.org/10.5753/wscad.2017.244.
