Infrastructure-as-Code for Data-Intensive Architectures: A Model-Driven Development Approach

Artač, Matej; Borovšak, Tadej; Nitto, Elisabetta Di; Guerriero, Michele; Pérez-Palacín, Diego; Tamburri, Damian A.

doi:10.1109/icsa.2018.00025

Cited by 24 publications

(9 citation statements)

References 11 publications

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“…On the other hand, LEMMA provides technology-specific provisioning artifacts that can be used with different cloud providers. DICER [2] represents an approach based on technology-independent models for the generation of infrastructure as code and is used to deploy the software system. DICER models encapsulate monitoring, self-adaptation, configuration management, server deployment, and software system deployment.…”

Section: Related Workmentioning

confidence: 99%

“…While various larger enterprises like Netflix, 1 Spotify, 2 or Zalando 3 regularly report about their successful adoption of MSA, there are only a small number of experience reports (e.g., [12]) about how microservices combined with DevOps can be successfully implemented in small and medium-sized development organizations (SMOs) with less than 100 developers involved. Such SMOs typically do not have sufficient resources to directly apply large-scale process models such as Scrum at Scale [14,77] in terms of employees, knowledge, and experience.…”

Section: Introductionmentioning

confidence: 99%

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Applying Model-Driven Engineering to Stimulate the Adoption of DevOps Processes in Small and Medium-Sized Development Organizations

et al. 2021

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Microservice architecture (MSA) denotes an increasingly popular architectural style in which business capabilities are wrapped into autonomously developable and deployable software components called microservices. Microservice applications are developed by multiple DevOps teams each owning one or more services. In this article, we explore the state of how DevOps teams in small and medium-sized organizations (SMOs) cope with MSA and how they can be supported. We show through a secondary analysis of an exploratory interview study comprising six cases, that the organizational and technological complexity resulting from MSA poses particular challenges for small and medium-sized organizations (SMOs). We apply model-driven engineering to address these challenges. As results of the second analysis, we identify the challenge areas of building and maintaining a common architectural understanding, and dealing with deployment technologies. To support DevOps teams of SMOs in coping with these challenges, we present a model-driven workflow based on LEMMA—the Language Ecosystem for Modeling Microservice Architecture. To implement the workflow, we extend LEMMA with the functionality to (i) generate models from API documentation; (ii) reference remote models owned by other teams; (iii) generate deployment specifications; and (iv) generate a visual representation of the overall architecture. We validate the model-driven workflow and our extensions to LEMMA through a case study showing that the added functionality to LEMMA can bring efficiency gains for DevOps teams. To develop best practices for applying our workflow to maximize efficiency in SMOs, we plan to conduct more empirical research in the field in the future.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Applying Model-Driven Engineering to Stimulate the Adoption of DevOps Processes in Small and Medium-Sized Development Organizations

et al. 2021

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“…On the other hand, LEMMA provides technology-specific provisioning artifacts that can be used with different cloud providers. DICER [1] represents an approach based on technology-independent models for the generation of infrastructure as code and is used to deploy the software system. DICER models encapsulate monitoring, self-adaptation, configuration management, server deployment, and software system deployment.…”

Section: Msa Operation Modelingmentioning

confidence: 99%

Applying Model-Driven Engineering to Stimulate the Adoption of DevOps Processes in Small and Medium-Sized Development Organizations

Sorgalla¹,

Wizenty²,

Rademacher³

et al. 2021

Preprint

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Purpose: Microservice Architecture (MSA) denotes an increasingly popular architectural style in which business capabilities are wrapped into autonomously developable and deployable software components called microservices. Microservice applications are developed by multiple DevOps teams each owning one or more services. In this article, we explore the state of how DevOps teams in small and medium-sized organizations (SMOs) cope with MSA and how they can be supported. Methods: We show through a secondary analysis of an exploratory interview study comprising six cases, that Attention. This is a preprint draft version.

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“…A fullfledged example of DTSM modeling and related analysis is, instead, presented in [47,29] where we have shown how the profile is suitable for capturing the characteristics of Apache Storm [58] applications. Finally, [22] focuses on DDSM modeling and on the generation of the corresponding blueprint. All in all, main contributions of this paper are the presentation of the complete and consolidated DIA profile that takes into account the main characteristics of DIAs and of the most used technologies adopted for their development, as well as the definition of the methodological steps followed to develop such profile and also methodological step for practitioners to develop models at DTSM and DDSM levels.…”

Section: Approach Overviewmentioning

confidence: 99%

“…In [29] we describe how a different Stormbased case study can be modeled at the DPIM and DTSM level. In [22] we have shown how we can model at the DDSM level a Storm-based and a Spark-based deployment of WikiStats that exploits Cassandra as a storage node. In general, changing technologies means adopting the architectural style typical of that technology, exploiting the corresponding profile stereotype, and personalize, if needed, the properties associated to the technology.…”

Section: Uml Ddsm Profilementioning

confidence: 99%

A UML Profile for the Design, Quality Assessment and Deployment of Data-intensive Applications

et al. 2019

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Big Data or Data-Intensive applications (DIAs) seek to mine, manipulate, extract or otherwise exploit the potential intelligence hidden behind Big Data. However, several practitioner surveys remark that DIAs potential is still untapped because of very difficult and costly design, quality assessment, and continuous refinement. To address the above shortcoming, we propose the use of a UML domain-specific modelling language or profile specifically tailored to support the design, assessment, and continuous deployment of DIAs. This article illustrates our DIA-specific profile and outlines its usage in the context of DIA performance engineering and deployment. For DIA performance engineering, we rely on the Apache Hadoop technology, while for DIA deployment, we leverage the TOSCA language. We conclude that the proposed profile offers a powerful language for data-intensive software and systems modelling, quality evaluation and automated deployment of DIAs on private or public clouds.

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Infrastructure-as-Code for Data-Intensive Architectures: A Model-Driven Development Approach

Cited by 24 publications

References 11 publications

Applying Model-Driven Engineering to Stimulate the Adoption of DevOps Processes in Small and Medium-Sized Development Organizations

Applying Model-Driven Engineering to Stimulate the Adoption of DevOps Processes in Small and Medium-Sized Development Organizations

Applying Model-Driven Engineering to Stimulate the Adoption of DevOps Processes in Small and Medium-Sized Development Organizations

A UML Profile for the Design, Quality Assessment and Deployment of Data-intensive Applications

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