MUSA Deployer: Deployment of Multi-cloud Applications

Casola, Valentina; Benedictis, Alessandra De; Rak, Massimiliano; Villano, Umberto; Rios, Erkuden; Rego, Angel; Capone, Giancarlo

doi:10.1109/wetice.2017.46

Cited by 9 publications

(5 citation statements)

References 8 publications

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“…Moreover, CloudFM has a Cloud Provider-Specific Model (CPSM) that uses a model@run-time engine to request cloud providers a list of available resources to refine the CPIM into a CPSM. Casola et al (2017) provide a DevOps approach to developing multi-cloud applications (MUSA) with Service Level Agreements (SLAs). MUSA has a modeler tool to define application architectures and deployment requirements using a modeling language based on CAMEL (Rossini, 2015) to define application architectures and deployment requirements.…”

Section: Related Workmentioning

confidence: 99%

Modeling Cloud Infrastructure Provisioning: A Software-as-a-Service Approach

Sandobalín,

Iñiguez-Jarrín

2023

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Provisioning means making an infrastructure element, such as a server or network device, ready for use. DevOps community leverages the Infrastructure as Code (IaC) approach to supply tools for cloud infrastructure provision. However, each provisioning tool has its scripting language, and managing different tools for several cloud providers is time-consuming and error-prone. In previous work, we presented a model-driven infrastructure provisioning tool called ARGON, which leverages the IaC approach using Model-Driven Engineering. ARGON provides a modeling language to specify cloud infrastructure resources and generates scripts to support cloud infrastructure provisioning orchestration. Since ARGON runs in the Eclipse Desktop IDE, we propose to migrate from an ARGON Desktop to an ARGON Cloud as a Software-as-a-Service approach. On the one hand, we developed a domain-specific modeling language using JavaScript Frameworks. On the other hand, we used a Model-to-Text transformation engine through a REST web service to generate scripts. Finally, we carried out an example by modeling infrastructure resources for Amazon Web Services and then generating a script for the Ansible tool.

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Section: Related Workmentioning

confidence: 99%

Modeling Cloud Infrastructure Provisioning: A Software-as-a-Service Approach

Sandobalín,

Iñiguez-Jarrín

2023

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“…In particular, the model-driven approach supports automation according to the IaC approach. For instance, some of the research efforts made to manage infrastructure resources, and software deployment based on model-driven techniques, are CloudMF [20], MUSA Deployer [21] and MORE [22]. Additionally, Bernal et al [23] propose a UML-based framework that can be used to model a cloud system, including the underlying infrastructure, the user resource requirements and their interactions with the cloud provider.…”

Section: Related Workmentioning

confidence: 99%

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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“…Ferry et al [7] in his paper presents the "model-driven provisioning, deployment, monitoring, and adaptation of multi-cloud systems." Pitchai, et al [8] provides the "methods to predict the availability and the integrity of the data in the cloud using the soft computing" Mukhopadhyay et al [9] presents the "architecture that enables the Secure Cloud Computing with Elliptic Curve Cryptography" Junghanns et al [10] Casola, et al [11] et al the author proffers "MUSA deployer: deployment of multi-cloud applications." The Jiao et al [12] in his paper present the method of the "Multi-objective data placement for multi-cloud socially aware services."…”

Section: Related Workmentioning

confidence: 99%

“…They are capable of handling variety of computing and storage necessities, paving way to build more complex systems that are supported by heterogeneous services provided by diverse service renders associated with the cloud offering the end users with the services that are value added. The multi cloud applications are the examples of one such application that involves the resources of the multiple cloud providers that are independent and does not require any co-operation among the involved service providers of the cloud [11]. The fig.…”

Section: Proposed Modelmentioning

confidence: 99%

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Efficient Information Maintenance Using Computational Intelligence in the Multi-Cloud Architecture

Raj¹

2019

JSCP

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The multi cloud architecture improvises the constancy of the system associated with the cloud computing as they offer benefits such as the reducing the severity of the vendor lock in, which is the major impediment to the cloud service adoption. However the information maintenance in the multi cloud architecture is a very tedious and challenging as it inherits multitudes of complexity due to various services rendered, interfaces and technologies used. As storage is the fundamental usage of the cloud computing, the failures in the information maintenance (storage, securing, retrieval) in the traditional cloud computing has led to the development of different techniques that are highly reliable and flexible. The paper also scope in developing one such adaptable and highly secure information maintenance using the Cryptography integrated Computational intelligence (CICI) for application deployment in the multi cloud architecture. The proposed method is validated using the network simulator –Two, to verify the performance augmentation of the multi-cloud architecture in its information maintenance.

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MUSA Deployer: Deployment of Multi-cloud Applications

Cited by 9 publications

References 8 publications

Modeling Cloud Infrastructure Provisioning: A Software-as-a-Service Approach

Modeling Cloud Infrastructure Provisioning: A Software-as-a-Service Approach

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

Efficient Information Maintenance Using Computational Intelligence in the Multi-Cloud Architecture

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