Advertisement

Towards Model-Driven Infrastructure Provisioning for Multiple Clouds

  • J. SandobalinEmail author
  • E. Insfran
  • S. Abrahao
Conference paper
  • 206 Downloads
Part of the Lecture Notes in Information Systems and Organisation book series (LNISO, volume 34)

Abstract

Companies currently use cloud services to obtain access to computing resources located in virtualized environments. Practitioners and researchers are adopting the Infrastructure as Code approach to cloud infrastructure automation, in addition to attaining the infrastructure for a particular cloud provider in a short amount of time. However, the traditional method of using a single cloud provider has several limitations concerning privacy, security, performance, geographical reach, and vendor lock-in. In order to mitigate these issues, industry and academia are implementing multiple clouds (i.e., multi-cloud). In a previous work, we introduced ARGON, which is an infrastructure modeling tool for cloud provisioning that leverages Model-Driven Engineering to provide a uniform, cohesive, and seamless process with which to support the DevOps approach. In this paper, we present an extension of ARGON that can be employed to support multi-cloud infrastructure provisioning modeling and propose a model-driven approach that allows migration among cloud providers.

Keywords

Infrastructure provisioning Infrastructure as code Cloud computing Multi-Cloud DevOps Model-Driven engineering 

Notes

Acknowledgements

This research is supported by Adapt@Cloud (TIN2017-84550-R) project, in addition to the SENESCYT and the Escuela Politécnica Nacional (Ecuador).

References

  1. 1.
    Humble, J., Farley, D.: Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation. Addison-Wesley Professional (2010)Google Scholar
  2. 2.
    Morris, K.: Infrastructure as Code: Managing Servers in the Cloud. O’Reilly Media (2016)Google Scholar
  3. 3.
    Brikman, Y.: Terraform: Up and Running. O’Reilly Media (2017)Google Scholar
  4. 4.
    Grozev, N., Buyya, R.: Multi-Cloud Provisioning and Load Distribution for Three-Tier Applications. ACM Trans. Auton. Adapt, Syst (2014)CrossRefGoogle Scholar
  5. 5.
    Sandobalin, J., Insfran, E., Abrahao, S.: An Infrastructure modelling tool for cloud provisioning. In: Proceedings—IEEE 14th International Conference on Services Computing, SCC. pp. 354–361. Hawai (2017)Google Scholar
  6. 6.
    AWS CloudFormation, https://aws.amazon.com/cloudformation/. Accessed 25 July 2018
  7. 7.
    AWS OpsWorks, https://aws.amazon.com/opsworks/. Accessed 25 July 2018
  8. 8.
    Ferry, N., Rossini, A.: CloudMF: model-driven management of multi-cloud applications. ACM Trans. Internet Technol. 18(2), 16–24 (2018)CrossRefGoogle Scholar
  9. 9.
    Casola, V., De Benedictis, A., Rak, M., Villano, U., Rios, E., Rego, A., Capone, G.: MUSA deployer: Deployment of multi-cloud applications. In: Proceedings—IEEE 26th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises, WETICE. pp. 107–112. IEEE (2017)Google Scholar
  10. 10.
    Rossini, A.: Cloud application modelling and execution language (CAMEL) and the PaaSage workflow. In: Proceedings—European Conference on Service-Oriented and Cloud Computing, ESOCC. pp. 437–439. Springer Verlag, Italy (2016)Google Scholar
  11. 11.
    Nitto, E.Di, Matthews, P., Petcu, D., Solberg, A.: Model-Driven Development and Operation of Multi-Cloud Applications. Springer International Publishing, Cham (2017)CrossRefGoogle Scholar
  12. 12.
    Chen, W., Liang, C., Wan, Y., Gao, C., Wu, G., Wei, J., Huang, T.: MORE: A model-driven operation service for cloud-based IT systems. In: Proceedings—IEEE 13th International Conference on Services Computing, SCC. pp. 633–640. IEEE (2016)Google Scholar
  13. 13.
    Kolovos, D.S., García-Domínguez, A., Rose, L.M., Paige, R.F.: Eugenia: towards disciplined and automated development of GMF-based graphical model editors. Softw. Syst. Model. 16(1), 229–255 (2015)CrossRefGoogle Scholar
  14. 14.
    Graphical Modeling Framework (GMF) Tooling, https://www.eclipse.org/gmf-tooling/. Accessed 22 April 2018
  15. 15.
    Steinberg, D., Budinsky, F., Merks, E., Paternostro, M.: EMF: eclipse modeling framework (2008)Google Scholar
  16. 16.
    Jouault, F., Allilaire, F., Bézivin, J., Kurtev, I.: ATL: A model transformation tool. Sci. Comput. Program. 72(1–2), 31–39 (2008)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Acceleo, https://www.eclipse.org/acceleo/. Accessed 25 July 2018

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Escuela Politécnica NacionalQuitoEcuador
  2. 2.Universitat Politècnica de ValènciaValenciaSpain

Personalised recommendations