Scientific computing applications usually need huge amounts of computational power. The cloud provides interesting high-performance computing solutions, with its promise of virtually infinite resources on demand. However, migrating scientific computing problems to clouds and the re-creation of software environment on the vendor-supplied OS and cloud instances is often a laborious task. It is also assumed that the scientist who is performing the experiments has significant knowledge of computer science, cloud computing and the migration procedure, which is often not true. Considering these obstacles, we have designed a tool suite that migrates the complete software environment directly to the cloud. The developed desktop-to-cloud-migration (D2CM) tool supports transformation and migration of virtual machine images, reusable deployment description and life-cycle management for applications to be hosted on Amazon Cloud or compatible infrastructure such as Eucalyptus. The paper also presents an electrochemical case study and computational experiments targeted at designing modern supercapacitors. These experiments have extensively used the tool in drawing domain specific results. Detailed analysis of the case showed that D2CM tool not only simplifies the migration procedure for the scientists, but also helps them in optimizing the calculations and compute clusters, by providing them a new dimension -- cost-to-value of computational experiments.
Cloud Modelling Framework (CloudMF) @cite_58 is a model-based framework being developed at SINTEF (http: www.sintef.no) for modeling the provisioning of cloud resources for complex software systems and their deployment and control in multi-cloud platforms. CloudMF provides a tool-supported domain-specific modeling language CloudML to model the provisioning and deployment of multi-cloud systems and a models@run-time environment for enacting the provisioning, deployment, and adaptation of these systems. The deployment of the underlying modeling language CloudML has been partially supported by three European FP7 projects (REMICS, MODAClouds and PaaSage), where we were also involved, which all try to leverage its potential for simplifying the deployment and control of cloud applications.
@cite_58
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Dynamically adaptive systems (DAS) enable the continuous design and adaptation of complex software systems, but their main focus is limited to the application itself rather than the underlying platform and infrastructure. Cloud computing, in contrast, enables the management of the complete software stack, but it lacks integration with software engineering approaches, techniques, and methods from DAS. Model-based approaches have been successfully adopted for modelling DAS at design-time and facilitate their adaptation at run-time. Therefore, a natural next step is to adopt model-based approaches to enable cloud-based DAS. In this paper, we present the Cloud Modelling Framework (CloudMF), a model-based framework that addresses this issue. It consists of (i) a tool-supported domain-specific modelling language to model the provisioning and deployment of multi-cloud systems, and (ii) a models@run-time environment for enacting the provisioning, deployment and adaptation of these systems.