Análise de Precificação de Recursos Utilizados em Computação em Nuvem
Resumo
Os provedores de nuvem geralmente oferecem uma grande variedade de recursos a seus usuários. Nesse cenário, a seleção de um recurso inapropriado pode levar a perdas financeiras e/ou tempos de resposta longos. Sendo assim, a utilização de uma estratégia eficiente de seleção de recursos em nuvem pode trazer importantes benefícios para os seus usuários. Neste artigo, são apresentados experimentos com o objetivo de se determinar a influência de características do processador e da memória na composição do custo de diferentes recursos disponíveis nos provedores Amazon EC2 e Google GCE. Tal análise pode ser incorporada em estratégias que visam ao mesmo tempo a diminuição do custo financeiro e a manutenção de bom desempenho para as aplicações em nuvem.Referências
Amazon (2016), Amazon EC2 Instance Pricing. http://aws.amazon.com/ec2/pricing/.
Arevalos, S., López-Pires, F., Baran, B. (2016). A Comparative Evaluation of Algorithms for Auction-Based Cloud Pricing Prediction. IEEE International Conference on Cloud Engineering, pages 99-108.
Foster, I., Zaho Y., Raicu, I., and Lu. S. (2008). Cloud computing and grid computing 360- degree compared. In Grid Computing Environments Workshop, pages 1–10.
Google (2016), Google Compute Engine Pricing. http://cloud.google.com/compute/pricing.
Jain. R. (1991) The Art of Computer System Performance Analysis: Techniques for Experimental Design, Measurement, Simulation and Modeling, John Wiley & Sons.
Kheradmand, S., Meybodi, M. R. (2014). Costumer Needs Aware Pricing Strategy for a Cloud Provider. 4th IEEE International Conference on Computer and Knowledge Engineering, pages 334-339.
Lee, G., Chun, B., Katz, R. H. (2011) Heterogeneity-Aware Resource Allocation and Scheduling in the Cloud, Proceedings of the 3rd USENIX conference on Hot topics in cloud (HotCloud), pages 1-10.
Leite, A. F. (2014), A User-Centered and Autonomic Multi-Cloud Architecture for High Performance Computing Applications, PhD Thesis, Université Paris-Sud and Universidade de Brasília, available at //hal.inria.fr/tel-01097295.
Leite, A. F., Alves, V., Rodrigues, G. N., Tadonki, C., Eisenbeis, C. and Melo, A. C. M. A. (2015), Automating Resource Selection and Configuration in Inter-clouds through a Software Product Line Method. 8th IEEE International Conference on Cloud Computing, pages 726-733, 2015.
Mell, P. and Grance, T. (2011) The NIST definition of Cloud Computing, National Institute of Standards and Technology. Technical Report SP800-145, NIST Information Technology Laboratory.
Ostermann S., Iosup, A., Yigitbasi, N., Prodan, R., Fahringer, T. and Epema, D. (2009). A performance analysis of EC2 cloud computing services for scientic computing. In International Conference on Cloud Computing, pages 115–131.
Sotomayor B., Montero, R. S., Llorente, I. M. and Foster (2009), I. Virtual infrastructure management in private and hybrid clouds. IEEE Internet Computing,13(5), pages 14–22.
Tanaka, M., Murakami, Y. (2014) Strategy-proof Pricing for Cloud Service Composition. IEEE Transactions on Cloud Computing, 99(PP), pages 1-15.
Zhang, M., Ranjan, R., Nepal S., Menzel, M. and Haller (2012), A., A Declarative Recommender System for Cloud Infrastructure Services Selection, 9th Int. Conf. on Economics of Grids, Clouds, Systems, and Services (GECON), pages 102-113.
Arevalos, S., López-Pires, F., Baran, B. (2016). A Comparative Evaluation of Algorithms for Auction-Based Cloud Pricing Prediction. IEEE International Conference on Cloud Engineering, pages 99-108.
Foster, I., Zaho Y., Raicu, I., and Lu. S. (2008). Cloud computing and grid computing 360- degree compared. In Grid Computing Environments Workshop, pages 1–10.
Google (2016), Google Compute Engine Pricing. http://cloud.google.com/compute/pricing.
Jain. R. (1991) The Art of Computer System Performance Analysis: Techniques for Experimental Design, Measurement, Simulation and Modeling, John Wiley & Sons.
Kheradmand, S., Meybodi, M. R. (2014). Costumer Needs Aware Pricing Strategy for a Cloud Provider. 4th IEEE International Conference on Computer and Knowledge Engineering, pages 334-339.
Lee, G., Chun, B., Katz, R. H. (2011) Heterogeneity-Aware Resource Allocation and Scheduling in the Cloud, Proceedings of the 3rd USENIX conference on Hot topics in cloud (HotCloud), pages 1-10.
Leite, A. F. (2014), A User-Centered and Autonomic Multi-Cloud Architecture for High Performance Computing Applications, PhD Thesis, Université Paris-Sud and Universidade de Brasília, available at //hal.inria.fr/tel-01097295.
Leite, A. F., Alves, V., Rodrigues, G. N., Tadonki, C., Eisenbeis, C. and Melo, A. C. M. A. (2015), Automating Resource Selection and Configuration in Inter-clouds through a Software Product Line Method. 8th IEEE International Conference on Cloud Computing, pages 726-733, 2015.
Mell, P. and Grance, T. (2011) The NIST definition of Cloud Computing, National Institute of Standards and Technology. Technical Report SP800-145, NIST Information Technology Laboratory.
Ostermann S., Iosup, A., Yigitbasi, N., Prodan, R., Fahringer, T. and Epema, D. (2009). A performance analysis of EC2 cloud computing services for scientic computing. In International Conference on Cloud Computing, pages 115–131.
Sotomayor B., Montero, R. S., Llorente, I. M. and Foster (2009), I. Virtual infrastructure management in private and hybrid clouds. IEEE Internet Computing,13(5), pages 14–22.
Tanaka, M., Murakami, Y. (2014) Strategy-proof Pricing for Cloud Service Composition. IEEE Transactions on Cloud Computing, 99(PP), pages 1-15.
Zhang, M., Ranjan, R., Nepal S., Menzel, M. and Haller (2012), A., A Declarative Recommender System for Cloud Infrastructure Services Selection, 9th Int. Conf. on Economics of Grids, Clouds, Systems, and Services (GECON), pages 102-113.
Publicado
05/10/2016
Como Citar
PORTELLA, Gustavo; RODRIGUES, Genaína; DE MELO, Alba.
Análise de Precificação de Recursos Utilizados em Computação em Nuvem. In: SIMPÓSIO EM SISTEMAS COMPUTACIONAIS DE ALTO DESEMPENHO (SSCAD), 17. , 2016, Aracajú.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2016
.
p. 179-190.
DOI: https://doi.org/10.5753/wscad.2016.14258.
