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. We model a web application to support analysis and simulation of cloud environments.We implement the model as an extension of the CloudSim simulator.We found that the user behavior has a strong influence on the resource utilization.We found Generalized Extreme Value and Generalized Lambda distributions instead of Exponential distribution to represent session time. Display Omitted Workload modeling enables performance analysis and simulation of cloud resource management policies, which allows cloud providers to improve their systems' Quality of Service (QoS) and researchers to evaluate new policies without deploying expensive large scale environments. However, workload modeling is challenging in the context of cloud computing due to the virtualization layer overhead, insufficient tracelogs available for analysis, and complex workloads. These factors contribute to a lack of methodologies and models to characterize applications hosted in the cloud. To tackle the above issues, we propose a web application model to capture the behavioral patterns of different user profiles and to support analysis and simulation of resources utilization in cloud environments. A model validation was performed using graphic and statistical hypothesis methods. An implementation of our model is provided as an extension of the CloudSim simulator. Cloud computing is a recent advancement wherein IT infrastructure and applications are provided as ‘services’ to end-users under a usage-based payment model. It can leverage virtualized services even on the fly based on requirements (workload patterns and QoS) varying with time. The application services hosted under Cloud computing model have complex provisioning, composition, configuration, and deployment requirements. Evaluating the performance of Cloud provisioning policies, application workload models, and resources performance models in a repeatable manner under varying system and user configurations and requirements is difficult to achieve. To overcome this challenge, we propose CloudSim: an extensible simulation toolkit that enables modeling and simulation of Cloud computing systems and application provisioning environments. The CloudSim toolkit supports both system and behavior modeling of Cloud system components such as data centers, virtual machines (VMs) and resource provisioning policies. It implements generic application provisioning techniques that can be extended with ease and limited effort. Currently, it supports modeling and simulation of Cloud computing environments consisting of both single and inter-networked clouds (federation of clouds). Moreover, it exposes custom interfaces for implementing policies and provisioning techniques for allocation of VMs under inter-networked Cloud computing scenarios. Several researchers from organizations, such as HP Labs in U.S.A., are using CloudSim in their investigation on Cloud resource provisioning and energy-efficient management of data center resources. The usefulness of CloudSim is demonstrated by a case study involving dynamic provisioning of application services in the hybrid federated clouds environment. The result of this case study proves that the federated Cloud computing model significantly improves the application QoS requirements under fluctuating resource and service demand patterns. Copyright © 2010 John Wiley & Sons, Ltd. Cloud computing provides users near instant access to seemingly unlimited resources, and provides service providers the opportunity to deploy complex information technology infrastructure, as a service, to their customers. Providers bene t from economies of scale and multiplexing gains a orded by sharing of resources through virtualization of the underlying physical infrastructure. However, the scale and highly dynamic nature of cloud platforms impose signi cant new challenges to cloud service providers. In particular, realizing sophisticated cloud services requires a cloud control framework that can orchestrate cloud resource provisioning, con guration, utilization and decommissioning across a distributed set of physical resources. In this paper we advocate a data-centric approach to cloud orchestration. Following this approach, cloud resources are modeled as structured data that can be queried by a declarative language, and updated with well-de ned transactional semantics. We examine the feasibility, bene ts and challenges of the approach, and present our design and prototype implementation of the Data-centric Management Framework (DMF) as a solution, with data models, query languages and semantics that are speci cally designed for cloud resource orchestration.