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Method, formalization, and algorithms to split topology models for distributed cloud application deployments

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Abstract

For automating the deployment of applications in cloud environments, a variety of technologies have been developed in recent years. These technologies enable to specify the desired deployment in the form of deployment models that can be automatically processed by a provisioning engine. However, the deployment across several clouds increases the complexity of the provisioning. Using one deployment model with a single provisioning engine, which orchestrates the deployment across the clouds, forces the providers to expose low-level APIs to ensure the accessibility from outside. In this paper, we present an extended version of the split and match method to facilitate the division of deployment models to multiple models which can be deployed by each provider separately. The goal of this approach is to reduce the information and APIs which have to be exposed to the outside. We present a formalization and algorithms to automate the method. Moreover, we validate the practical feasibility by a prototype based on the TOSCA standard and the OpenTOSCA ecosystem.

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Notes

  1. https://www.chef.io/chef/.

  2. https://www.docker.com/.

  3. https://github.com/OpenTOSCA.

  4. It is called “declarative” in contrast to “imperative” deployment modeling requiring defined workflows.

  5. http://alien4cloud.github.io/.

  6. https://cloudify.co/.

  7. https://github.com/eclipse/winery.

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Acknowledgements

This work is partially funded by the BMWi Projects SmartOrchestra (01MD16001F) and IC4F (01MA17008G).

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  1. University of Stuttgart, Universitätsstrasse 38, 70569, Stuttgart, Germany

    Karoline Saatkamp, Uwe Breitenbücher, Oliver Kopp & Frank Leymann

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  1. Karoline Saatkamp
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  2. Uwe Breitenbücher
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Correspondence to Karoline Saatkamp.

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Saatkamp, K., Breitenbücher, U., Kopp, O. et al. Method, formalization, and algorithms to split topology models for distributed cloud application deployments. Computing 102, 343–363 (2020). https://doi.org/10.1007/s00607-019-00721-8

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