Master Thesis MSTR-2021-41

BibliographyDonsuypae, Nakharin: Decentralized cross-organizational application deployment using multiple different deployment automation technologies.
University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Master Thesis No. 41 (2021).
65 pages, english.

With the rise of cloud computing, the automated deployment and management of applications has become increasingly important. Manual execution of deployment steps can become error-prone, time-consuming, and costly. Therefore, deployment technologies are necessary to achieve a high level of automation. However, the majority of these technologies have one common drawback, they all use a central orchestrator for the deployment execution. Most often, multiple departments or even companies participate in the deployment process. Moreover, most deployments are not based on workflow technologies, which restrict the orchestration capabilities as well as the customization of complex deployment logic that may be needed for the deployment of complex applications. Furthermore, often more than one deployment technology is used, e.g. Terraform for setting up the virtual machine and Kubernetes for running the applications on top of it. As a result, the orchestration of the deployment (i) between participants and (ii) different deployment technologies for the correct deployment execution and data exchange is necessary. To address these challenges, this work proposes an approach to enable decentralized cross-organizational application deployment based on multiple deployment technologies. This is done by (i) annotation of a deployment model with participant information and (ii) generation of participant-specific workflows orchestrating the deployment with different technologies and data exchange between participants. To prove the feasibility of this approach, a prototypical implementation and an accompanying case study is provided.

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Department(s)University of Stuttgart, Institute of Architecture of Application Systems
Superviser(s)Leymann, Prof. Frank; Wild, Karoline; Wurster, Michael
Entry dateNovember 4, 2021
   Publ. Computer Science