Orthogonal Variability Modeling to Support Multi-cloud Application Configuration

Jamshidi, Pooyan; Pahl, Claus

doi:10.1007/978-3-319-14886-1_23

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…Using variability models from SPLE to model deployment variants is a widely used approach [22,[38][39][40][41][42][43][44][45]. Thereby, multiple variability models along with dependencies between them can be used to manage different aspects of variability.…”

Section: Software Product Line Engineeringmentioning

confidence: 99%

Modeling Different Deployment Variants of a Composite Application in a Single Declarative Deployment Model

et al. 2022

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For automating the deployment of composite applications, typically, declarative deployment models are used. Depending on the context, the deployment of an application has to fulfill different requirements, such as costs and elasticity. As a consequence, one and the same application, i.e., its components, and their dependencies, often need to be deployed in different variants. If each different variant of a deployment is described using an individual deployment model, it quickly results in a large number of models, which are error prone to maintain. Deployment technologies, such as Terraform or Ansible, support conditional components and dependencies which allow modeling different deployment variants of a composite application in a single deployment model. However, there are deployment technologies, such as TOSCA and Docker Compose, which do not support such conditional elements. To address this, we extend the Essential Deployment Metamodel (EDMM) by conditional components and dependencies. EDMM is a declarative deployment model which can be mapped to several deployment technologies including Terraform, Ansible, TOSCA, and Docker Compose. Preprocessing such an extended model, i.e., conditional elements are evaluated and either preserved or removed, generates an EDMM conform model. As a result, conditional elements can be integrated on top of existing deployment technologies that are unaware of such concepts. We evaluate this by implementing a preprocessor for TOSCA, called OpenTOSCA Vintner, which employs the open-source TOSCA orchestrators xOpera and Unfurl to execute the generated TOSCA conform models.

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Section: Software Product Line Engineeringmentioning

confidence: 99%

Modeling Different Deployment Variants of a Composite Application in a Single Declarative Deployment Model

et al. 2022

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“…We extended our earlier work in to provide a generic framework. The pattern selection and migration plan formulation is embedded into an overarching migration process and a variability model that has been repurposed to support the pattern selection activity, see Figure . The process focuses on the identification of the pattern application or situational context, consisting of the organisation, the target software application and the selected cloud platform, captured as profiles and relevant migration constraints.…”

Section: Introductionmentioning

confidence: 99%

Pattern‐based multi‐cloud architecture migration

2016

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Many organizations migrate on-premise software applications to the cloud. However, current coarse-grained cloud migration solutions have made such migrations a non transparent task, an endeavor based on trial-anderror. This paper presents Variability-based, Pattern-driven Architecture Migration .V-PAM), a migration method based on (i) a catalogue of fine-grained service-based cloud architecture migration patterns that target multi-cloud, (ii) a situational migration process framework to guide pattern selection and composition, and (iii) a variability model to structure system migration into a coherent framework. The proposed migration patterns are based on empirical evidence from several migration projects, best practice for cloud architectures and a systematic literature review of existing research. Variability-based, Pattern-driven Architecture Migration allows an organization to (i) select appropriate migration patterns, (ii) compose them to define a migration plan, and (iii) extend them based on the identification of new patterns in new contexts. The patterns are at the core of our solution, embedded into a process model, with their selection governed by a variability model

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