Avaliação de Deep Learning para Predição de Mensagens Processadas pela Plataforma de Integração Guaraná
Resumo
Empresas possuem diversas aplicações em seus ecossistemas de softwares que precisam ser integradas. Plataformas de integração permitem uma eficiência para realizar a integração, entretanto, algumas configurações devem ser definidas de forma manual, como o número de threads disponíveis. Este artigo apresenta um estudo experimental que criou e avaliou modelos de deep learning para realizar a predição do número de mensagens que serão processadas a partir de um determinado número de threads e da taxa de mensagens, para permitir uma definição de forma automática e em tempo de execução do número de threads nas plataformas de
Palavras-chave:
Integração de Aplicações Empresariais, Inteligência Artificial, Otimização de EAI
Referências
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y., and Zheng, X.(2016). Tensorflow: A system for large-scale machine learning. In 12th USENIX Symp. on Operating Systems Design and Implementation (OSDI 16), pages 265–283.
Beer, M. I. and Hassan, M. F. (2018). Adaptive security architecture for protecting restful web services in enterprise computing environment. Service Oriented Computing and Applications, 12(2):111–121.
Dossot, D., d’Emic, J., and Romero, V. (2014).Mule in action. Manning Publications Co.
Fisher, M., Partner, J., Bogoevici, M., and Fuld, I. (2012). Spring integration in action. Manning Publications Co.
Frantz, R. Z. (2012). Enterprise application integration: an easy-to-maintain model-driven engineering approach. PhD thesis, Universidad de Sevilla.
Frantz, R. Z., Corchuelo, R., Basto-Fernandes, V., Rosa-Sequeira, F., Roos-Frantz, F., and Arjona, J. L. (2020). A cloud-based integration platform for enterprise application integration: a model-driven engineering approach. Software: Practice and Experience. (in-press), 1(1):1–25.
Frantz, R. Z., Corchuelo, R., and Roos-Frantz, F. (2016). On the design of a maintainable software development kit to implement integration solutions. Journal of Systems and Software, 111(1):89–104.
Frantz, R. Z., Sawicki, S., Roos-Frantz, F., Yevseyeva, I., and Emmerich, M. (2015). On using markov decision processes to model integration solutions for disparate resources in software ecosystems. In Intl. Conf. on Enterprise Information Systems (ICEIS), pages 260–268.
Géron, A. (2017). Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems. ”O’Reilly Media, Inc.”.
Hohpe, G. and Woolf, B. (2004). Enterprise integration patterns: Designing, building, and deploying messaging solutions. Addison-Wesley Professional.
Ibsen, C. and Anstey, J. (2018). Camel in action. Manning Publications Co.
Kingma, D. P. and Ba, J. (2014). Adam: A method for stochastic optimization. In 3rd Intl. Conf. for Learning Representations (ICLR), pages 1–13.
Manikas, K. (2016). Revisiting software ecosystems research: A longitudinal literature study. Journal of Systems and Software, 117:84–103.
Nemirovsky, D., Arkose, T., Markovic, N., Nemirovsky, M., Unsal, O., and Cristal, A. (2017). A machine learning approach for performance prediction and scheduling on heterogeneous cpus. In 29th Intl. Symp. on Computer Architecture and High Performance Computing (SBAC-PAD), pages 121–128.
Page, A. J. and Naughton, T. J. (2005). Framework for task scheduling in heterogeneous distributed computing using genetic algorithms. Artificial Intelligence Review,24(3):415–429.
Beer, M. I. and Hassan, M. F. (2018). Adaptive security architecture for protecting restful web services in enterprise computing environment. Service Oriented Computing and Applications, 12(2):111–121.
Dossot, D., d’Emic, J., and Romero, V. (2014).Mule in action. Manning Publications Co.
Fisher, M., Partner, J., Bogoevici, M., and Fuld, I. (2012). Spring integration in action. Manning Publications Co.
Frantz, R. Z. (2012). Enterprise application integration: an easy-to-maintain model-driven engineering approach. PhD thesis, Universidad de Sevilla.
Frantz, R. Z., Corchuelo, R., Basto-Fernandes, V., Rosa-Sequeira, F., Roos-Frantz, F., and Arjona, J. L. (2020). A cloud-based integration platform for enterprise application integration: a model-driven engineering approach. Software: Practice and Experience. (in-press), 1(1):1–25.
Frantz, R. Z., Corchuelo, R., and Roos-Frantz, F. (2016). On the design of a maintainable software development kit to implement integration solutions. Journal of Systems and Software, 111(1):89–104.
Frantz, R. Z., Sawicki, S., Roos-Frantz, F., Yevseyeva, I., and Emmerich, M. (2015). On using markov decision processes to model integration solutions for disparate resources in software ecosystems. In Intl. Conf. on Enterprise Information Systems (ICEIS), pages 260–268.
Géron, A. (2017). Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems. ”O’Reilly Media, Inc.”.
Hohpe, G. and Woolf, B. (2004). Enterprise integration patterns: Designing, building, and deploying messaging solutions. Addison-Wesley Professional.
Ibsen, C. and Anstey, J. (2018). Camel in action. Manning Publications Co.
Kingma, D. P. and Ba, J. (2014). Adam: A method for stochastic optimization. In 3rd Intl. Conf. for Learning Representations (ICLR), pages 1–13.
Manikas, K. (2016). Revisiting software ecosystems research: A longitudinal literature study. Journal of Systems and Software, 117:84–103.
Nemirovsky, D., Arkose, T., Markovic, N., Nemirovsky, M., Unsal, O., and Cristal, A. (2017). A machine learning approach for performance prediction and scheduling on heterogeneous cpus. In 29th Intl. Symp. on Computer Architecture and High Performance Computing (SBAC-PAD), pages 121–128.
Page, A. J. and Naughton, T. J. (2005). Framework for task scheduling in heterogeneous distributed computing using genetic algorithms. Artificial Intelligence Review,24(3):415–429.
Publicado
11/11/2020
Como Citar
REHBEIN, Matheus H.; FRANTZ, Rafael Z. .
Avaliação de Deep Learning para Predição de Mensagens Processadas pela Plataforma de Integração Guaraná. In: ESCOLA REGIONAL DE ENGENHARIA DE SOFTWARE (ERES), 4. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 164-173.
DOI: https://doi.org/10.5753/eres.2020.13727.
