An Infrastructure Modelling Tool for Cloud Provisioning

Sandobalin, Julio; Insfrán, Emilio; Abrahão, Silvia

doi:10.1109/scc.2017.52

Cited by 26 publications

(18 citation statements)

References 9 publications

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“…This section provides an overview of the infrastructure provisioning tools selected as treatments. We selected Argon [7] because this tool has been proposed in order to abstract the complexity of working with different tools for infrastructure provisioning. It implements a domain-specific language so as to model the characteristics of the cloud infrastructure and provides transformation engines with which to automate the infrastructure provisioning for different cloud providers.…”

Section: Tools Being Comparedmentioning

confidence: 99%

“…In a previous work [7], we proposed an infrastructure modeling approach and tool called Argon for cloud provisioning, which leverages model-driven engineering and supports the IaC approach. The main contributions of our approach are: i) it abstracts the complexity of managing the particularities of different cloud providers in order to define the required infrastructure by using a domain-specific modeling language called ArgonML (Argon Modeling Language), and ii) it generates the IaC scripts using Argon in order to support cloud infrastructure provisioning.…”

Section: Introductionmentioning

confidence: 99%

“…The purpose of this paper is, therefore, to report a family of experiments carried out to compare the effectiveness and the user perceptions of two IaC tools: Ansible [11] (a code-centric tool) and Argon [7] (a model-driven tool) in terms of their support for the definition of cloud infrastructure resources. Note that the IaC approach has two stages (i.e., definition and provisioning), but that in this paper we focus solely on the definition stage.…”

Section: Introductionmentioning

confidence: 99%

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On the Effectiveness of Tools to Support Infrastructure as Code: Model-Driven Versus Code-Centric

2020

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Infrastructure as Code (IaC) is an approach for infrastructure automation that is based on software development practices. The IaC approach supports code-centric tools that use scripts to specify the creation, updating and execution of cloud infrastructure resources. Since each cloud provider offers a different type of infrastructure, the definition of an infrastructure resource (e.g., a virtual machine) implies writing several lines of code that greatly depend on the target cloud provider. Model-driven tools, meanwhile, abstract the complexity of using IaC scripts through the high-level modeling of the cloud infrastructure. In a previous work, we presented an infrastructure modeling approach and tool (Argon) for cloud provisioning that leverages model-driven engineering and supports the IaC approach. The objective of the present work is to compare a model-driven tool (Argon) with a well-known code-centric tool (Ansible) in order to provide empirical evidence of their effectiveness when defining the cloud infrastructure, and the participants' perceptions when using these tools. We, therefore, conducted a family of three experiments involving 67 Computer Science students in order to compare Argon with Ansible as regards their effectiveness, efficiency, perceived ease of use, perceived usefulness, and intention to use. We used the AB/BA crossover design to configure the individual experiments and the linear mixed model to statistically analyze the data collected and subsequently obtain empirical findings. The results of the individual experiments and metaanalysis indicate that Argon is more effective as regards supporting the IaC approach in terms of defining the cloud infrastructure. The participants also perceived that Argon is easier to use and more useful for specifying the infrastructure resources. Our findings suggest that Argon accelerates the provisioning process by modeling the cloud infrastructure and automating the generation of scripts for different DevOps tools when compared to Ansible, which is a code-centric tool that is greatly used in practice. INDEX TERMS Infrastructure as code, DevOps, model-driven engineering, controlled experiments, crossover design, linear mixed model. JULIO SANDOBALÍN received the M.Sc. degree in software engineering from the Universitat Politècnica de València, Spain, in 2014, where he is currently pursuing the Ph.D. degree with the Software Engineering and Information Systems Research Group. His research activities and interests include a wide range of subjects related to model-driven engineering, cloud computing, DevOps, and empirical software engineering. His research is currently focused on the continuous delivery of cloud resources based on model-driven engineering and DevOps.

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Section: Tools Being Comparedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the Effectiveness of Tools to Support Infrastructure as Code: Model-Driven Versus Code-Centric

2020

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“…In order to make it easier for customers to select services and deploy applications, cloud modelling languages (CMLs) were proposed (e.g. [12,13,14,15,16,17]). They provide means for composing a high level description of a cloud application topology, then automate their deployment accordingly.…”

Section: In Termsmentioning

confidence: 99%

“…CAMEL [15] can be viewed as a 'superset' CML as it integrates and extends existing DSLs. ARGON [16], addresses the issue of abstracting the complexity of using CMLs by enabling users to specify infrastructure resources then generating deployment code, similar to the SLO-ML approach. CadaML [29] is used to manage multi-tenant architecture evolution by transforming an abstract model into the appropriate code for different cloud data storage types.…”

Section: Related Workmentioning

confidence: 99%

SLO-ML: A Language for Service Level Objective Modelling in Multi-cloud Applications

Elhabbash

Jumagaliyev²,

Blair

et al. 2019

Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing

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Cloud modelling languages (CMLs) are designed to assist customers in tackling the diversity of services in the cloud market. While many CMLs have been proposed in the literature, they lack practical support for automating the selection of services based on the specific service level objectives of a customer's application. We put forward SLO-ML, a novel and generative CML to capture service level requirements and, subsequently, to select the services to honour customer requirements and generate the deployment code appropriate to these services. We present the architectural design of SLO-ML and the associated broker that realises the deployment operations. We rigorously evaluate SLO-ML using a mixed methods approach. First, we exploit an experimental case study with a group of researchers and developers using a real-world cloud application. We also assess overheads through an exhaustive set of empirical scalability tests. Through expressing the levels of gained productivity and experienced usability, we highlight SLO-ML's profound potential in enabling user-centric cloud brokers. We also discuss limitations as application requirements grow.

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Stepwise Adoption of Continuous Delivery in Model-Driven Engineering

Garcia

Cabot

2019

Software Engineering Aspects of Continuous Development and New Paradigms of Software Production and Deployment

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An Infrastructure Modelling Tool for Cloud Provisioning

Cited by 26 publications

References 9 publications

On the Effectiveness of Tools to Support Infrastructure as Code: Model-Driven Versus Code-Centric

On the Effectiveness of Tools to Support Infrastructure as Code: Model-Driven Versus Code-Centric

SLO-ML: A Language for Service Level Objective Modelling in Multi-cloud Applications

Stepwise Adoption of Continuous Delivery in Model-Driven Engineering

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