Análise de Desempenho de um Simulador de Reservatórios de Petróleo em um Ambiente de Computação em Nuvem
Resumo
A computação em nuvem (cloud computing) é considerada como um novo paradigma de computação distribuída em que os clientes podem acessar recursos diretamente da Internet. Estudos recentes avaliam o uso dos recursos providos pela nuvem para executar aplicações científicas que demandam alto poder computacional. Neste sentido, este trabalho apresenta uma análise de desempenho de um simulador de reservatórios de petróleo a fim de avaliar o comportamento de tal aplicação científica em um ambiente de computação em nuvem provido pelas plataformas Amazon EC2 e Microsoft Azure. Essa análise leva em conta a observação de métricas do sistema operacional e a execução de benchmarks específicos. Os resultados mostram que o overhead de virtualização e o compartilhamento de recursos causam um descréscimo significativo de desempenho em tais aplicações.Referências
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Heilmann, Z., Deidda, G., Satta, G., and Bonomi, E. (2013). Real-time imaging and data analysis for shallow seismic data using a cloud-computing portal. Near Surface Geophysics, 11(4):407–421.
Hennessy, J. L. and Patterson, D. A. (2012). Computer architecture: a quantitative ap proach. Elsevier.
Hoffa, C., Mehta, G., Freeman, T., Deelman, E., Keahey, K., Berriman, B., and Good, J. (2008). On the use of cloud computing for scientic workows. In eScience, 2008. eScience’08. IEEE Fourth International Conference on, pages 640–645. IEEE.
Jackson, K. R., Ramakrishnan, L., Muriki, K., Canon, S., Cholia, S., Shalf, J., Wasserman, H. J., and Wright, N. J. (2010). Performance analysis of high performance computing applications on the amazon web services cloud. In Cloud Computing Technology and Science (CloudCom), 2010 IEEE Second International Conference on, pages 159–168. IEEE.
Knight, D., Shams, K., Chang, G., and Soderstrom, T. (2012). Evaluating the efcacy of the cloud for cluster computation. In Aerospace Conference, 2012 IEEE, pages 1–10. IEEE.
Ostermann, S., Iosup, A., Yigitbasi, N., Prodan, R., Fahringer, T., and Epema, D. (2010). A performance analysis of ec2 cloud computing services for scientic computing. In Cloud Computing, pages 115–131. Springer.
Peaceman, D. W. (2000). Fundamentals of numerical reservoir simulation. Elsevier.
Popiolek, P. F. and Mendizabal, O. M. (2013). Monitoring and analysis of performance impact in virtualized environments. Journal of Applied Computing Research, 2(2):75– 82.
Ramakrishnan, L., Zbiegel, P. T., Campbell, S., Bradshaw, R., Canon, R. S., Coghlan, S., Sakrejda, I., Desai, N., Declerck, T., and Liu, A. (2011). Magellan: experiences from a science cloud. In Proceedings of the 2nd international workshop on Scientic cloud computing, pages 49–58. ACM.
Subramanian, V., Ma, H., Wang, L., Lee, E.-J., and Chen, P. (2011). Rapid 3d seismic source inversion using windows azure and amazon ec2. In Services (SERVICES), 2011 IEEE World Congress on, pages 602–606. IEEE.
Vecchiola, C., Pandey, S., and Buyya, R. (2009). High-performance cloud computing: In Pervasive Systems, Algorithms, and Networks A view of scientic applications. (ISPAN), 2009 10th International Symposium on, pages 4–16. IEEE.
Vöckler, J.-S., Juve, G., Deelman, E., Rynge, M., and Berriman, B. (2011). Experiences using cloud computing for a scientic workow application. In Proceedings of the 2nd international workshop on Scientic cloud computing, pages 15–24. ACM.
Deshane, T., Shepherd, Z., Matthews, J., Ben-Yehuda, M., Shah, A., and Rao, B. (2008). Quantitative comparison of xen and kvm. Xen Summit, Boston, MA, USA, pages 1–2.
He, Q., Zhou, S., Kobler, B., Duffy, D., and McGlynn, T. (2010). Case study for running hpc applications in public clouds. In Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing, pages 395–401. ACM.
Heilmann, Z., Deidda, G., Satta, G., and Bonomi, E. (2013). Real-time imaging and data analysis for shallow seismic data using a cloud-computing portal. Near Surface Geophysics, 11(4):407–421.
Hennessy, J. L. and Patterson, D. A. (2012). Computer architecture: a quantitative ap proach. Elsevier.
Hoffa, C., Mehta, G., Freeman, T., Deelman, E., Keahey, K., Berriman, B., and Good, J. (2008). On the use of cloud computing for scientic workows. In eScience, 2008. eScience’08. IEEE Fourth International Conference on, pages 640–645. IEEE.
Jackson, K. R., Ramakrishnan, L., Muriki, K., Canon, S., Cholia, S., Shalf, J., Wasserman, H. J., and Wright, N. J. (2010). Performance analysis of high performance computing applications on the amazon web services cloud. In Cloud Computing Technology and Science (CloudCom), 2010 IEEE Second International Conference on, pages 159–168. IEEE.
Knight, D., Shams, K., Chang, G., and Soderstrom, T. (2012). Evaluating the efcacy of the cloud for cluster computation. In Aerospace Conference, 2012 IEEE, pages 1–10. IEEE.
Ostermann, S., Iosup, A., Yigitbasi, N., Prodan, R., Fahringer, T., and Epema, D. (2010). A performance analysis of ec2 cloud computing services for scientic computing. In Cloud Computing, pages 115–131. Springer.
Peaceman, D. W. (2000). Fundamentals of numerical reservoir simulation. Elsevier.
Popiolek, P. F. and Mendizabal, O. M. (2013). Monitoring and analysis of performance impact in virtualized environments. Journal of Applied Computing Research, 2(2):75– 82.
Ramakrishnan, L., Zbiegel, P. T., Campbell, S., Bradshaw, R., Canon, R. S., Coghlan, S., Sakrejda, I., Desai, N., Declerck, T., and Liu, A. (2011). Magellan: experiences from a science cloud. In Proceedings of the 2nd international workshop on Scientic cloud computing, pages 49–58. ACM.
Subramanian, V., Ma, H., Wang, L., Lee, E.-J., and Chen, P. (2011). Rapid 3d seismic source inversion using windows azure and amazon ec2. In Services (SERVICES), 2011 IEEE World Congress on, pages 602–606. IEEE.
Vecchiola, C., Pandey, S., and Buyya, R. (2009). High-performance cloud computing: In Pervasive Systems, Algorithms, and Networks A view of scientic applications. (ISPAN), 2009 10th International Symposium on, pages 4–16. IEEE.
Vöckler, J.-S., Juve, G., Deelman, E., Rynge, M., and Berriman, B. (2011). Experiences using cloud computing for a scientic workow application. In Proceedings of the 2nd international workshop on Scientic cloud computing, pages 15–24. ACM.
Publicado
2014-10-08
Como Citar
ALVES, Maicon; DRUMMOND, Lúcia Maria.
Análise de Desempenho de um Simulador de Reservatórios de Petróleo em um Ambiente de Computação em Nuvem.
Anais do Simpósio em Sistemas Computacionais de Alto Desempenho (SSCAD), [S.l.], p. 51-62, out. 2014.
ISSN 0000-0000.
Disponível em: <https://sol.sbc.org.br/index.php/sscad/article/view/14999>. Acesso em: 18 maio 2024.
doi: https://doi.org/10.5753/wscad.2014.14999.
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