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A Model-Driven Approach for Interoperability Among SaaS and DaaS/DBaaS: The MIDAS Case

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Abstract

Cloud Platforms are heterogeneous, and users may face interoperability issues migrating applications or exchanging data among distinct clouds due, for instance, to the lack of standards solutions. Several solutions have been proposed to overcome lock-in situations, and middleware platforms are one of them. A semantic interoperability solution named middleware for Data as a Service (DaaS)/Database as a Service (DBaaS) and Software as a Service (SaaS)—MIDAS has been developed to overcome this lock-in issue. It is an intermediate communication layer to retrieve data from DaaS or DBaaS through a Structured Query Language (SQL) or a Not Only SQL (NoSQL) created at the SaaS level. MIDAS is a platform for software execution, but software development needs support for its entire life cycle. Therefore, we propose the Model drIven Approach for MIDAS (MIAMI), which enables the specification of platform-independent middleware models and their use to generate code on different cloud platforms. MIAMI comprises a Domain-Specific Modeling Language (DSML) that enables middleware models and a transformation specification, which defines how these models can be converted to code. MIAMI offers a strategy for MIDAS specification and code generation phases to help middleware developers’ activities. MIAMI was applied to code generation specifications in Cloud Foundry, Amazon Web Services, OpenShift, and Heroku providers to evaluate our approach. This specification shows MIAMI’s feasibility and points out that MDD is a promising approach to improving cloud interoperability solutions.

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Notes

  1. https://www.google.com/intl/pt-BR/drive/.

  2. https://cloud.google.com/appengine.

  3. https://www.ibm.com/br-pt/cloud/bluemix.

  4. https://aws.amazon.com/pt/ec2/.

  5. https://aws.amazon.com/pt/.

  6. https://azure.microsoft.com/pt-br/.

  7. https://www.redhat.com/pt-br/topics/containers/red-hat-openshift-kubernetes.

  8. https://www.openstack.org/.

  9. https://cloudify.co/.

  10. https://puppet.com/.

  11. https://www.chef.io/products/chef-infra/.

  12. https://www.ansible.com/.

  13. https://www.omg.org/mda/.

  14. https://www.heroku.com/.

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Acknowledgements

The authors would like to thank FAPESB (Foundation for Research Support of the State of Bahia) (Grant nos. BOL, TIC 02/2015) for financial support. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) – Finance Code 001.

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Authors and Affiliations

  1. Formalisms and Semantic Applications Research Group (FORMAS), Institute of Computing, Federal University of Bahia, Av. Milton Santos, Ondina, Salvador, Bahia, 40170-110, Brazil

    Babacar Mane, Gustavo Quinteiro & Daniela Barreiro Claro

  2. Models for Software Engineering (MAtisse), Department of Exact Sciences and Earth, State University of Bahia, 2555 Silveira Martins St., Cabula, Salvador, Bahia, Brazil

    Ana Patrícia F. Magalhães Mascarenhas

  3. Post Graduate Program in Computing and Systems, Salvador University, 251 Dr. Jose Peroba St., Salvador, Bahia, Brazil

    Ana Patrícia F. Magalhães Mascarenhas

  4. Models for Software Engineering (MAtisse), Institute of Computing, Federal University of Bahia, Av. Milton Santos, Ondina, Salvador, Bahia, 40170-110, Brazil

    Rita Suzana Pitangueira Maciel

Authors
  1. Babacar Mane
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  2. Ana Patrícia F. Magalhães Mascarenhas
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  3. Gustavo Quinteiro
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  4. Rita Suzana Pitangueira Maciel
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  5. Daniela Barreiro Claro
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Correspondence to Daniela Barreiro Claro.

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This work has a financial support of FAPESB (Foundation for Research Support of the State of Bahia).

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This article is part of the topical collection “Enterprise Information Systems” guest edited by Michal Smialek, Slimane Hammoudi, Alexander Brodsky and Joaquim Filipe.

Appendix

Appendix

List 2 identifies the clauses employed in the user SQL query and defines them as variables of type array.

figure b

List 3 identifies the dataset types defined in the user query.

figure c

List 4 shows a piece of PHP code responsible to send the request to the DaaS provider and get the result at the variable $data_result.

figure d

List 5 shows the getFilterResult($daasResult, $filters, and $dataset) functions in a PHP code.

figure e

List 6 shows the filtersToArray($filter) functions in a PHP code.

figure f

List 7 presents the piece of PHP code responsible to format the user data into XML, JSON or CSV.

figure g

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Mane, B., Mascarenhas, A.P.F.M., Quinteiro, G. et al. A Model-Driven Approach for Interoperability Among SaaS and DaaS/DBaaS: The MIDAS Case. SN COMPUT. SCI. 3, 301 (2022). https://doi.org/10.1007/s42979-022-01185-y

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