Kubow: Um Serviço de Autoadaptação Baseada em Arquitetura para Aplicações Nativas da Nuvem
Resumo
Este artigo apresenta Kubow, uma instanciação do arcabouço de autoadaptação baseada em arquitetura Rainbow para aplicações nativas de nuvem implantadas em contêineres gerenciados pelo Kubernetes.
Referências
Aderaldo, C. M. (2019). Kubow: Um Serviço de Autoadaptação Baseada em Arquitetura para Aplicações Implantadas com Docker e Kubernetes. Master’s thesis, Universidade de Fortaleza, Programa de Pós-Graduação em Informática Aplicada.
Brewer, E. (2018). Kubernetes and the New Cloud (Invited Keynote). In SIGMOD’18. Cheng, S.-W. and Garlan, D. (2012). Stitch: A language for architecture-based self-adaptation. Journal of Systems and Software, 85(12):2860–2875.
Delnat, W. et al. (2018). K8-scalar: a workbench to compare autoscalers for container-orchestrated database clusters. In IEEE/ACM SEAMS’18, pages 33–39. IEEE.
Florio, L. and Di Nitto, E. (2016). Gru: An approach to introduce decentralized autonomic behavior in microservices architectures. In IEEE ICAC’16, pages 357–362. IEEE.
Garlan, D. et al. (2004). Rainbow: Architecture-based self-adaptation with reusable infrastructure. Computer, 37(10):46–54.
Garlan, D., Monroe, R. T., and Wile, D. (2000). Acme: Architectural description of component-based systems. Foundations of Component-Based Systems, 68:47–68.
Kephart, J. O. and Chess, D. M. (2003). The vision of autonomic computing. Computer, 36(1):41–50.
Mendonça, N. C. et al. (2018). Generality vs. reusability in architecture-based selfadaptation: The case for self-adaptive microservices. In ECSA’18: Comp. Proceedings.
Pahl, C. et al. (2017). Cloud container technologies: a state-of-the-art review. IEEE Trans. Cloud Computing.
Rodriguez, M. A. and Buyya, R. (2018). Containers orchestration with cost-efficient autoscaling in cloud computing environments. arXiv preprint arXiv:1812.00300.
Sampaio Jr, A. R. et al. (2018). Improving Microservice-based Applications with Runtime Placement Adaptation. Journal of Internet Services and Applications.
Weyns, D. (2019). Software Engineering of Self-adaptive Systems, pages 399–443. Springer International Publishing.
Brewer, E. (2018). Kubernetes and the New Cloud (Invited Keynote). In SIGMOD’18. Cheng, S.-W. and Garlan, D. (2012). Stitch: A language for architecture-based self-adaptation. Journal of Systems and Software, 85(12):2860–2875.
Delnat, W. et al. (2018). K8-scalar: a workbench to compare autoscalers for container-orchestrated database clusters. In IEEE/ACM SEAMS’18, pages 33–39. IEEE.
Florio, L. and Di Nitto, E. (2016). Gru: An approach to introduce decentralized autonomic behavior in microservices architectures. In IEEE ICAC’16, pages 357–362. IEEE.
Garlan, D. et al. (2004). Rainbow: Architecture-based self-adaptation with reusable infrastructure. Computer, 37(10):46–54.
Garlan, D., Monroe, R. T., and Wile, D. (2000). Acme: Architectural description of component-based systems. Foundations of Component-Based Systems, 68:47–68.
Kephart, J. O. and Chess, D. M. (2003). The vision of autonomic computing. Computer, 36(1):41–50.
Mendonça, N. C. et al. (2018). Generality vs. reusability in architecture-based selfadaptation: The case for self-adaptive microservices. In ECSA’18: Comp. Proceedings.
Pahl, C. et al. (2017). Cloud container technologies: a state-of-the-art review. IEEE Trans. Cloud Computing.
Rodriguez, M. A. and Buyya, R. (2018). Containers orchestration with cost-efficient autoscaling in cloud computing environments. arXiv preprint arXiv:1812.00300.
Sampaio Jr, A. R. et al. (2018). Improving Microservice-based Applications with Runtime Placement Adaptation. Journal of Internet Services and Applications.
Weyns, D. (2019). Software Engineering of Self-adaptive Systems, pages 399–443. Springer International Publishing.
Publicado
25/09/2019
Como Citar
ADERALDO, Carlos; MENDONCA, Nabor.
Kubow: Um Serviço de Autoadaptação Baseada em Arquitetura para Aplicações Nativas da Nuvem. In: SESSÃO DE FERRAMENTAS - CONGRESSO BRASILEIRO DE SOFTWARE: TEORIA E PRÁTICA (CBSOFT), 1. , 2019, Salvador.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 120-125.
DOI: https://doi.org/10.5753/cbsoft_estendido.2019.7668.
