Uso de Aprendizado de Máquina Automatizado para Seleção de Provedores de Nuvem
Resumo
Neste trabalho, uma forma de ranqueamento e seleção de provedores de nuvem é apresentada por meio do uso da função de Aprendizado de Máquina Automatizado (AutoML) da plataforma H2O. Ele exibe um sistema de ranqueamento que produz uma pontuação para cada provedor de nuvem avaliado, a partir da qualificação dos requisitos exigidos pelo usuário. Experimentos realizados com o auxílio da plataforma H2O, levando em consideração o treinamento e análise de modelos de regressão, apresentam resultados precisos e mais rápidos quando comparados a alternativa de resolução determinística exata.
Palavras-chave:
Aprendizado de máquina, Nuvem Computacional, AutoML, Provedor de Nuvem, Ranqueamento
Referências
de Moraes, L. B., Fiorese, A., and Matos, F. (2017). A multi-criteria scoring method based on performance indicators for cloud computing provider selection. In ICEIS (2), pages 588–599.
Elshawi, R., Maher, M., and Sakr, S. (2019). Automated machine learning: State-of-theart and open challenges. arXiv preprint arXiv:1906.02287.
Ghafori, V. and Sarhadi, R. M. (2013). Best cloud provider selection using integrated anp-dematel and prioritizing smi attributes. International Journal of Computer Applications, 71(16).
H2O.ai (2018). H2O Driverless AI documentation. https://docs.huihoo.com/h2o/driverless-ai/userguide/1.0/index.html. Acessado em: 2021- 06-01.
H2O.ai (2021). H2O documentation. https://docs.h2o.ai/h2o/latest-stable/h2o-docs/index.html. Acessado em: 2021-06-01.
Lang, M., Wiesche, M., and Krcmar, H. (2016). What are the most important criteria for cloud service provider selection? a delphi study. In Proceedings of the 2016 European Conference on Information Systems (ECIS 2016), page 119. Association for Information Systems (AIS).
Mariani, G., Anghel, A., Jongerius, R., and Dittmann, G. (2017). Predicting cloud performance for hpc applications: A user-oriented approach. In 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), pages 524–533. IEEE.
Orlenko, A., Kofink, D., Lyytikäinen, L.-P., Nikus, K., Mishra, P., Kuukasjärvi, P., Karhunen, P. J., Kähönen, M., Laurikka, J. O., Lehtimäki, T., et al. (2020). Model selection for metabolomics: predicting diagnosis of coronary artery disease using automated machine learning. Bioinformatics, 36(6):1772–1778.
Repschläger, J., Wind, S., Zarnekow, R., and Turowski, K. (2011). Developing a cloud provider selection model. Enterprise modelling and information systems architectures (EMISA 2011).
Ribeiro, R., Pilastri, A., Moura, C., Rodrigues, F., Rocha, R., Morgado, J., and Cortez, P. (2020). Predicting physical properties of woven fabrics via automated machine learning and textile design and finishing features. In IFIP International Conference on Artificial Intelligence Applications and Innovations, pages 244–255. Springer.
Elshawi, R., Maher, M., and Sakr, S. (2019). Automated machine learning: State-of-theart and open challenges. arXiv preprint arXiv:1906.02287.
Ghafori, V. and Sarhadi, R. M. (2013). Best cloud provider selection using integrated anp-dematel and prioritizing smi attributes. International Journal of Computer Applications, 71(16).
H2O.ai (2018). H2O Driverless AI documentation. https://docs.huihoo.com/h2o/driverless-ai/userguide/1.0/index.html. Acessado em: 2021- 06-01.
H2O.ai (2021). H2O documentation. https://docs.h2o.ai/h2o/latest-stable/h2o-docs/index.html. Acessado em: 2021-06-01.
Lang, M., Wiesche, M., and Krcmar, H. (2016). What are the most important criteria for cloud service provider selection? a delphi study. In Proceedings of the 2016 European Conference on Information Systems (ECIS 2016), page 119. Association for Information Systems (AIS).
Mariani, G., Anghel, A., Jongerius, R., and Dittmann, G. (2017). Predicting cloud performance for hpc applications: A user-oriented approach. In 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), pages 524–533. IEEE.
Orlenko, A., Kofink, D., Lyytikäinen, L.-P., Nikus, K., Mishra, P., Kuukasjärvi, P., Karhunen, P. J., Kähönen, M., Laurikka, J. O., Lehtimäki, T., et al. (2020). Model selection for metabolomics: predicting diagnosis of coronary artery disease using automated machine learning. Bioinformatics, 36(6):1772–1778.
Repschläger, J., Wind, S., Zarnekow, R., and Turowski, K. (2011). Developing a cloud provider selection model. Enterprise modelling and information systems architectures (EMISA 2011).
Ribeiro, R., Pilastri, A., Moura, C., Rodrigues, F., Rocha, R., Morgado, J., and Cortez, P. (2020). Predicting physical properties of woven fabrics via automated machine learning and textile design and finishing features. In IFIP International Conference on Artificial Intelligence Applications and Innovations, pages 244–255. Springer.
Publicado
18/07/2021
Como Citar
HOPFER, Kauã B.; FIORESE, Adriano.
Uso de Aprendizado de Máquina Automatizado para Seleção de Provedores de Nuvem. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 48. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 9-18.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2021.15802.
