Automated Generation of Management Workflows for Applications Based on Deployment Models

To automate the deployment of applications several deployment technologies have been developed. However, the management of deployed applications is only partially covered by existing approaches: While management functionalities such as scaling components or changing their configurations are covered...

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Bibliographic Details
Published in2019 IEEE 23rd International Enterprise Distributed Object Computing Conference (EDOC) pp. 216 - 225
Main Authors Harzenetter, Lukas, Breitenbucher, Uwe, Leymann, Frank, Saatkamp, Karoline, Weder, Benjamin, Wurster, Michael
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2019
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Summary:To automate the deployment of applications several deployment technologies have been developed. However, the management of deployed applications is only partially covered by existing approaches: While management functionalities such as scaling components or changing their configurations are covered directly by cloud providers or configuration management technologies such as Chef, holistic management processes that affect multiple components probably deployed in different environments cannot be automated using these approaches. For example, testing all deployed components and their communication or backing up the entire application state that is scattered across different components requires custom management logic that needs to be implemented manually, e. g., using scripts. However, a manual implementation of such management processes is errorprone, time-consuming, and requires immense technical expertise. Therefore, we propose an approach that enables automatically generating executable management workflows based on the declarative deployment model of an application. This significantly reduces the effort for automating holistic management processes as no manual implementation is required. We validate the practical feasibility of the approach by a prototypical implementation based on the TOSCA standard and the OpenTOSCA ecosystem.
ISSN:2325-6362
DOI:10.1109/EDOC.2019.00034