Leveraging feature models to configure virtual appliances

Quinton, Clément; Rouvoy, Romain; Duchien, Laurence

doi:10.1145/2168697.2168699

Cited by 20 publications

(19 citation statements)

References 14 publications

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“…CAMEL integrates and extends existing DSLs, including Cloud Modelling Language (CloudML) [8,9], Saloon [14,15], and the Organisation part of CERIF [16], for specifying multiple aspects of multi-Cloud applications, such as provisioning, deployment, providers, organisations, users, and roles. Moreover, CAMEL adds DSLs for specifying aspects such as metrics, requirements, goals, scalability rules [17,18], security controls, execution contexts, execution histories, etc.…”

Section: Related Workmentioning

confidence: 99%

The MODAClouds Model-Driven Development

Ferry

Almeida

Solberg

2016

Model-Driven Development and Operation of Multi-Cloud Applications

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The Cloud computing market encompasses an ever-growing number of providers offering a multitude of infrastructure-as-a-service (IaaS) and platform-as-a-service (PaaS) solutions. In order to exploit the peculiarities of each Cloud solution as well as to optimize performances, availability, and cost, an emergent need is to run and manage multi-Cloud applications [1] (i.e., applications that can execute on multiple Cloud infrastructures and platforms). However, current stacks, libraries and frameworks lack in software engineering methodologies and tools to design, deploy and maintain multi-Cloud systems as stated in the CORDIS reports on Cloud computing [2,3], "whilst a distributed data environment (IaaS) cannot be easily moved to any platform provider (PaaS) […], it is also almost impossible to move a service/image/environment between providers on the same level."Model-Driven Development (MDD) [4] techniques are particularly useful to address these challenges. They allow shifting the paradigm from code-centric to model-centric. Models are thus the main artefacts of the development process and enable developers to work at a high level of abstraction, focusing on Cloud concerns rather than implementation details. Model transformations help automating the work of going from abstract concepts to implementation. This approach, which is commonly summarized as "model once, generate anywhere", is thus particularly relevant when it comes to design and management of applications across multiple Clouds, N. Ferry · A. Solberg (B)

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Section: Related Workmentioning

confidence: 99%

The MODAClouds Model-Driven Development

Ferry

Almeida

Solberg

2016

Model-Driven Development and Operation of Multi-Cloud Applications

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“…Although the presented example deals with the Heroku PaaS example, the approach proposed here is not specific to this cloud layer. Indeed, if the targeted cloud environment is a IaaS, our approach can be used as input to configure and manage virtual machines [15].…”

Section: Threats To Validitymentioning

confidence: 99%

Automated Selection and Configuration of Cloud Environments Using Software Product Lines Principles

Quinton

Romero

Duchien

2014

2014 IEEE 7th International Conference on Cloud Computing

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Abstract-Deploying an application to a cloud environment has recently become very trendy, since it offers many advantages such as improving reliability or scalability. These cloud environments provide a wide range of resources at different levels of functionality, which must be appropriately configured by stakeholders for the application to run properly. Handling this variability during the configuration and deployment stages is a complex and error-prone process, usually made in an ad hoc manner in existing solutions. In this paper, we propose a software product lines based approach to face these issues. Combined with a domain model used to select among cloud environments a suitable one, our approach supports stakeholders while configuring the selected cloud environment in a consistent way, and automates the deployment of such configurations through the generation of executable deployment scripts. To evaluate the soundness of the proposed approach, we conduct an experiment involving 10 participants with different levels of experience in cloud configuration and deployment. The experiment shows that using our approach significantly reduces time and most importantly, provides a reliable way to find a correct and suitable cloud configuration. Moreover, our empirical evaluation shows that our approach is effective and scalable to properly deal with a significant number of cloud environments.

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“…Quinton et al [16] use package dependency information to install the minimum number of packages for a given virtual appliance. To minimize the disk size, they estimate the installation size of each package.…”

Section: Vm Consolidationmentioning

confidence: 99%

Reducing VM Startup Time and Storage Costs by VM Image Content Consolidation

Razavi

Razorea

Kielmann

2014

Euro-Par 2013: Parallel Processing Workshops

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Abstract. Elastic cloud applications rely on fast virtual machine (VM) startup, e.g. when scaling out for handling increased workload. While there have been recent studies into the VM startup time in clouds, the effects of the VM image (VMI) disk size and its contents are little understood. To fill this gap, we present a detailed study of these factors on Amazon EC2. Based on our findings, we developed a novel approach for consolidating size and contents of VMIs. We then evaluated our approach with the ConPaaS VMI, an open-source Platform-as-a-Service runtime. Compared to an unmodified ConPaaS VMI, our approach results in up to four times reduction of the disk size, three times speedup for the VM startup time, and three times reduction of storage cost.

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Leveraging feature models to configure virtual appliances

Cited by 20 publications

References 14 publications

The MODAClouds Model-Driven Development

The MODAClouds Model-Driven Development

Automated Selection and Configuration of Cloud Environments Using Software Product Lines Principles

Reducing VM Startup Time and Storage Costs by VM Image Content Consolidation

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