Uma análise do consumo de energia de ambientes de processamento de dados massivos em nuvem
Resumo
As áreas de processamento de dados massivos (PDM, ou big-data) e de computação em nuvem têm se desenvolvido de forma integrada. Assim, PDM vem se consolidando como um dos maiores consumidores de recursos em datacenters que, por sua vez, são responsáveis por demandar em torno de 2% da energia global. Entender como elementos como o ambiente de virtualização e grau de paralelização dessas aplicações afetam esse consumo é, portanto, uma necessidade premente. Este artigo utiliza uma solução de monitoração abrangente para avaliar como o consumo de energia de aplicações big-data virtualizadas varia em função dos recursos alocados.
Referências
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Kgil, T., D’Souza, S., Saidi, A., Binkert, N., Dreslinski, R., Mudge, T., Reinhardt, S., and Flautner, K. (2006). Picoserver: using 3d stacking technology to enable a compact energy efficient chip multiprocessor. ACM SIGARCH Computer Architecture News, 34(5):117–128.
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Leverich, J. and Kozyrakis, C. (2010). On the energy (in) efficiency of hadoop clusters. ACM SIGOPS Operating Systems Review, 44(1):61–65.
Mashayekhy, L., Nejad, M. M., Grosu, D., Lu, D., and Shi, W. (2014). Energy-aware scheduling of mapreduce jobs. In Big Data (BigData Congress), 2014 IEEE International Congress on, pages 32–39. IEEE.
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Wirtz, T. and Ge, R. (2011). Improving mapreduce energy efficiency for computation intensive workloads. In Green Computing Conference and Workshops (IGCC), 2011 International, pages 1–8. IEEE.
Ye, K., Huang, D., Jiang, X., Chen, H., and Wu, S. (2010). Virtual machine based energy-efficient data center architecture for cloud computing: a performance perspective. In Proceedings of the 2010 IEEE/ACM Int’l Conference on Green Computing and Communications & Int’l Conference on Cyber, Physical and Social Computing, pages 171–178. IEEE Computer Society.
Zhang, Q., Cheng, L., and Boutaba, R. (2010). Cloud computing: state-of-the-art and research challenges. Journal of Internet Services and Applications, 1(1):7–18.
Andersen, D. G., Franklin, J., Kaminsky, M., Phanishayee, A., Tan, L., and Vasudevan, V. (2009). Fawn: A fast array of wimpy nodes. In Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles, pages 1–14. ACM.
Berral, J. L., Goiri, Í., Nguyen, T. D., Gavalda, R., Torres, J., and Bianchini, R. (2014). Building green cloud services at low cost. In Distributed Computing Systems (ICDCS), 2014 IEEE 34th International Conference on, pages 449–460. IEEE.
Conceição, V. S., Volpini, N. D. O., and Guedes, D. (2018). Seshat: uma arquitetura de monitoração escalável para ambientes em nuvem. In Anais do XVII Workshop em Desempenho de Sistemas Computacionais e de Comunicaç ao, Natal-RN. Sociedade Brasileira de Computaçao (SBC).
Dias, V., Meira, W., and Guedes, D. (2016). Dynamic reconfiguration of data parallel programs. In 2016 28th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), pages 190–197.
Dupont, C., Giuliani, G., Hermenier, F., Schulze, T., and Somov, A. (2012). An energy aware framework for virtual machine placement in cloud federated data centres. In Future Energy Systems: Where Energy, Computing and Communication Meet (e-Energy), 2012 Third International Conference on, pages 1–10. IEEE.
Ferro, M., Yokoyama, A., Klôh, V., Silva, G., Gandra, R., Bragança, R., Bulcao, A., Schulze, B., and SA-PETROBRAS, P. B. (2017). Analysis of gpu power consumption using internal sensors. In Anais do XVI Workshop em Desempenho de Sistemas Computacionais e de Comunicaç ao, Sao Paulo-SP. Sociedade Brasileira de Computaçao (SBC).
Goiri, I. n., Katsak, W., Le, K., Nguyen, T. D., and Bianchini, R. (2013). Parasol and greenswitch: Managing datacenters powered by renewable energy. In Proceedings of the Eighteenth International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS ’13, pages 51–64, New York, NY, USA. ACM.
Gu, X., Hou, R., Zhang, K., Zhang, L., and Wang, W. (2011). Application-driven energy-efficient architecture explorations for big data. In Proceedings of the 1st Workshop on Architectures and Systems for Big Data, pages 34–40. ACM.
Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., and Khan, S. U. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47:98–115.
Kansal, A., Zhao, F., Liu, J., Kothari, N., and Bhattacharya, A. A. (2010). Virtual machine power metering and provisioning. In Proceedings of the 1st ACM symposium on Cloud computing, pages 39–50. ACM.
Kgil, T., D’Souza, S., Saidi, A., Binkert, N., Dreslinski, R., Mudge, T., Reinhardt, S., and Flautner, K. (2006). Picoserver: using 3d stacking technology to enable a compact energy efficient chip multiprocessor. ACM SIGARCH Computer Architecture News, 34(5):117–128.
Kim, K. H., Beloglazov, A., and Buyya, R. (2009). Power-aware provisioning of cloud resources for real-time services. In Proceedings of the 7th International Workshop on Middleware for Grids, Clouds and e-Science, MGC ’09, pages 1:1–1:6, New York, NY, USA. ACM.
Koomey, J. (2011). Growth in data center electricity use 2005 to 2010. A report by Analytical Press, completed at the request of The New York Times, page 9. Disponível em [link].
Le, K., Bianchini, R., Zhang, J., Jaluria, Y., Meng, J., and Nguyen, T. D. (2011). Reducing electricity cost through virtual machine placement in high performance computing clouds. In Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis, page 22. ACM.
Leverich, J. and Kozyrakis, C. (2010). On the energy (in) efficiency of hadoop clusters. ACM SIGOPS Operating Systems Review, 44(1):61–65.
Mashayekhy, L., Nejad, M. M., Grosu, D., Lu, D., and Shi, W. (2014). Energy-aware scheduling of mapreduce jobs. In Big Data (BigData Congress), 2014 IEEE International Congress on, pages 32–39. IEEE.
Ramanathan, R. M. (2006). Intel R© multi-core processors. [link].
Sahoo, J., Mohapatra, S., and Lath, R. (2010). Virtualization: A survey on concepts, taxonomy and associated security issues. In Computer and Network Technology (ICCNT), 2010 Second International Conference on, pages 222–226. IEEE.
Shehabi, A., Smith, S. J., Sartor, D. A., Brown, R. E., Herrlin, M., Koomey, J. G., Masanet, E. R., Horner, N., Azevedo, I. L., and Lintner, W. (2016). United states data center energy usage report.
Whitney, J. and Delforge, P. (2014). Scaling up energy efficiency across the data center industry: Evaluating key drivers and barriers. [link]. Acessado em maio de 2015.
Wirtz, T. and Ge, R. (2011). Improving mapreduce energy efficiency for computation intensive workloads. In Green Computing Conference and Workshops (IGCC), 2011 International, pages 1–8. IEEE.
Ye, K., Huang, D., Jiang, X., Chen, H., and Wu, S. (2010). Virtual machine based energy-efficient data center architecture for cloud computing: a performance perspective. In Proceedings of the 2010 IEEE/ACM Int’l Conference on Green Computing and Communications & Int’l Conference on Cyber, Physical and Social Computing, pages 171–178. IEEE Computer Society.
Zhang, Q., Cheng, L., and Boutaba, R. (2010). Cloud computing: state-of-the-art and research challenges. Journal of Internet Services and Applications, 1(1):7–18.
Publicado
22/07/2018
Como Citar
VOLPINI, Nestor D. O.; CONCEIÇÃO, Vinicius S.; PONTES, Raphael L.; GUEDES, Dorgival.
Uma análise do consumo de energia de ambientes de processamento de dados massivos em nuvem. In: WORKSHOP EM DESEMPENHO DE SISTEMAS COMPUTACIONAIS E DE COMUNICAÇÃO (WPERFORMANCE), 17. , 2018, Natal.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 43-56.
ISSN 2595-6167.
DOI: https://doi.org/10.5753/wperformance.2018.3338.
