Master Thesis MSTR-2020-29

BibliographyDang, Phi: Live-modeling: enabling deployments at modeling time in OpenTOSCA.
University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Master Thesis No. 29 (2020).
94 pages, english.
Abstract

Due to the complex nature and heterogeneity of cloud computing, application development has become a convoluted task that requires detailed knowledge and a great amount of time. Currently, TOSCA, an OASIS standard, attempts to alleviate the involved nature of modeling by providing a meta-model for defining cloud applications. While this standard enables interoperability and portability, modeling these cloud services manually can still hinder many developers. Graphical modeling strives to resolve this by enabling users to visually construct models via an intuitive graphical user interface. Despite these efforts, application development is still impeded because users are obligated to interact with both the modeling and runtime systems in order to test and validate these models. However, the users’ workflow would be improved by decreasing the required oversight during development. Therefore, we devised a concept that consolidates the modeling and runtime systems where users have the option to deploy cloud applications during modeling time. Additionally, the concept incorporates a TOSCA-based validation system that verifies the configuration of an application model throughout the modeling process. We prototypically implemented our concept within the OpenTOSCA ecosystem by extending Winery’s topology modeler with the introduction of a visual feedback loop concerning the state of the deployment. Our prototype provides an alternative approach to managing TOSCA-based applications that creates a streamlined modeling process and makes developing complex applications more accessible.

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Department(s)University of Stuttgart, Institute of Architecture of Application Systems
Superviser(s)Leymann, Prof. Frank; Kepes, Kalman
Entry dateDecember 16, 2020
   Publ. Computer Science