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Improving cloud architectures using UML profiles and M2T transformation techniques

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Abstract

In this paper, we present an approach with the goal to improve the underlying architecture of cloud systems. For this, we propose UML2Cloud, a framework targeted at modeling and checking cloud systems. The main core of UML2Cloud uses UML profiles to capture the main elements of a cloud system including, among other elements, its underlying architecture and the interaction with clients. Additionally, UML2Cloud uses Model-to-Text transformation techniques to automatically generate configuration documents representing complex cloud scenarios. In this work, we use these documents as input for a cloud simulation tool, called Simcan2Cloud, to simulate the behavior of different systems. Thus, the analysis of the performance results obtained from the simulations allows us to draw some conclusions about how to improve the efficiency of the studied clouds by adjusting the hardware resource configuration.

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Notes

  1. Simcan2Cloud is being developed by the DTRS (Design and Testing Reliable Systems) research group of the Complutense University of Madrid and supervised by Dr. Alberto Núñez.

  2. Acceleo—Java Service Wrappers: https://wiki.eclipse.org/Acceleo/Getting_Started#Java_services_wrappers.

  3. Trace available at: http://www.cs.huji.ac.il/labs/parallel/workload/logs.html.

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Acknowledgements

This work was partially supported by the Spanish Ministry of Science and Innovation (cofinanced by European Union FEDER funds) projects DArDOS, reference TIN2015-65845-C3, subprojects 1-R and 2-R, FAME, reference RTI2018-093608-B, subprojects C31 and C32, TIN2015-66972-C5 subproject 2-R, the Comunidad de Madrid project FORTE-CM under Grant S2018/TCS-4314, and the Junta de Comunidades de Castilla-La Mancha (Spain) project SBPLY/17/180501/000276 (cofunded with FEDER funds, EU).

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Authors and Affiliations

  1. Albacete Research Institute of Informatics, Albacete, Spain

    Adrián Bernal, M. Emilia Cambronero & Valentín Valero

  2. Software Systems and Computation Department, Complutense University of Madrid, Madrid, Spain

    Alberto Núñez & Pablo C. Cañizares

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  1. Adrián Bernal
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Appendix

Appendix

1.1 Transformation templates and generated files

In this appendix, we show how a piece of a cloud model is transformed into several lines of text that belong to different parts of the scenario.ned configuration file. Let us consider the excerpt of a cloud model shown in Fig. 23. Component and stereotype property values are extracted through the implemented Acceleo templates, and then these are written in the scenario.ned file. To start with, the main template generateSimcan2CloudFiles (see Listing 4) extracts the information from the main component, that is CloudInfrastructure, and calls the generateNEDFile and generateINIFile templates. The generateNEDFile (see Listing 5) template creates the scenario.ned file. This template extracts the data centers referenced by the cloud infrastructure and calls the generateScenario and generateDataCenter templates to fill in the file.

The templates shown in Listings 4, 5, 6, 7, 8 and 9 write the part of the scenario.ned configuration file shown in Listing 10. In the same way, the templates shown in Listings 11, 12, 13 and 14 write the part of the scenario.ned configuration file shown in Listing 15.

Fig. 23
figure 23

A piece of a cloud system model

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Listing 4 Main Acceleo template.

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Listing 5 generateNEDFile Acceleo template.

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Listing 6 generateScenario Acceleo template.

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Listing 7 generateScenarioDataCenter Acceleo template.

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Listing 8 generateScenarioCloudProvider Acceleo template.

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Listing 9 generateScenarioConnections Acceleo template.

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Listing 10 Excerpt 1 from the generated .ned file.

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Listing 11 generateDataCenter Acceleo template.

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Listing 12 generateRackElement Acceleo template.

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Listing 13 generateDataCenterNetwork Acceleo template.

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Listing 14 generateRackConnection Acceleo template.

figure n

Listing 15 Excerpt 2 of the generated .ned file.

figure o

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Bernal, A., Cambronero, M.E., Núñez, A. et al. Improving cloud architectures using UML profiles and M2T transformation techniques. J Supercomput 75, 8012–8058 (2019). https://doi.org/10.1007/s11227-019-02980-w

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