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About the Complexity to Transfer Cloud Applications at Runtime and How Container Platforms Can Contribute?

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Cloud Computing and Service Science (CLOSER 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 864))

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Abstract

Cloud-native applications are often designed for only one specific cloud infrastructure or platform. The effort to port such kind of applications into a different cloud is usually a laborious one time exercise. Modern Cloud-native application architecture approaches make use of popular elastic container platforms (Apache Mesos, Kubernetes, Docker Swarm). These kind of platforms contribute to a lot of existing cloud engineering requirements. This given, it astonishes that these kind of platforms (already existing and open source available) are not considered more consequently for multi-cloud solutions. These platforms provide inherent multi-cloud support but this is often overlooked. This paper presents a software prototype and shows how Kubernetes and Docker Swarm clusters could be successfully transfered at runtime across public cloud infrastructures of Google (Google Compute Engine), Microsoft (Azure) and Amazon (EC2) and further cloud infrastructures like OpenStack. Additionally, software engineering lessons learned are derived and some astonishing performance data of the mentioned cloud infrastructures is presented that could be used for further optimizations of IaaS transfers of Cloud-native applications.

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Notes

  1. 1.

    To the best of the author’s knowledge there are no research papers analyzing this interesting case study. So, the reader is referred to an Wired magazine article: https://www.wired.com/2014/06/facebook-instagram/.

  2. 2.

    See https://docs.docker.com/v1.11/swarm/scheduler/filter/ (last access 15th Feb. 2017).

  3. 3.

    See https://kubernetes.io/docs/user-guide/node-selection/ (last access 15th Feb. 2017).

  4. 4.

    See https://mesosphere.github.io/marathon/docs/constraints.html (last access 15th Feb. 2017).

  5. 5.

    https://github.com/microservices-demo/microservices-demo (last access 3rd July 2017).

  6. 6.

    To get Kubernetes running in a multi-cloud scenario it is necessary to assign an additional virtual network interface with the public IP address of the node. Kubernetes provides no config options for that mode of operation! However, even these kind of obstacles can be transparently handled by drivers.

  7. 7.

    http://www.paasage.eu/ (visited 15th Feb. 2017).

  8. 8.

    In this case node termination (Azure blocking, OpenStack non-blocking).

  9. 9.

    According to the synergy 2016 Cloud Research Report http://bit.ly/2f2FsGK (visited 12th Jul. 2017).

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Acknowledgements

This research is funded by German Federal Ministry of Education and Research (13FH021PX4). I would like to thank Peter Quint, Christian Stüben, and Arne Salveter for their hard work and their contributions to the Project Cloud TRANSIT. Additionally I would like to thank the practitioners Mario-Leander Reimer and Josef Adersberger from QAWare for inspiring discussions and contributions concerning cloud-native application stacks.

Author information

Authors and Affiliations

  1. Center for Communication, Systems and Applications (CoSA), Lübeck University of Applied Sciences, Lübeck, Germany

    Nane Kratzke

Authors
  1. Nane Kratzke
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Corresponding author

Correspondence to Nane Kratzke .

Editor information

Editors and Affiliations

  1. Columbia University, New York, New York, USA

    Donald Ferguson

  2. Escola d’Enginyeria, Barcelona, Spain

    Víctor Méndez Muñoz

  3. Departamento de Engenharia Informatica, Universidade da Coimbra, Coimbra, Portugal

    Jorge Cardoso

  4. Dublin City University, Dublin 9, Ireland

    Markus Helfert

  5. Free University of Bozen-Bolzano, Bolzano, Bolzano, Italy

    Claus Pahl

Appendices

Appendix

1.1 Cluster Definition File (Intended state)

This exemplary cluster definition file defines a Swarm cluster with the intended state to be deployed in two districts provided by two providers GCE and AWS. It defines three type of user defined node types (flavors): small, med, and large. 3 master and 3 worker nodes should be deployed on small virtual machine types in district gce-europe. 10 worker nodes should be deployed on small virtual machine types in district aws-europe. The flavors small, med, large are defined in Appendix 7.

figure c

1.2 Resources File (Current state)

This exemplary resources file describes provided resources for the operated cluster. This example describes a simple one node cluster (1 master) being operated in one district (OpenStack). A security group was requested. Some data is omitted for better readability.

figure d

1.3 District Definition File (JSON)

The following and exemplary district definition defines provider specific settings and mappings. The user defined district gce-europe should be realized using the provider specific GCE zones europe-west1-b and europe-west1-c. Necessary and provider specific access settings like project identifiers, regions, and credentials are provided as well. User defined flavors (see cluster definition format above) are mapped to concrete provider specific machine types.

figure e

Credentials File (JSON)

The following and exemplary credential file provides access credentials for customer specific GCE and AWS accounts as identified by the district definition file (gce_default and aws_default).

figure f

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Kratzke, N. (2018). About the Complexity to Transfer Cloud Applications at Runtime and How Container Platforms Can Contribute?. In: Ferguson, D., Muñoz, V., Cardoso, J., Helfert, M., Pahl, C. (eds) Cloud Computing and Service Science. CLOSER 2017. Communications in Computer and Information Science, vol 864. Springer, Cham. https://doi.org/10.1007/978-3-319-94959-8_2

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