Moving Applications to the Cloud: An Approach Based on Application Model Enrichment

Leymann, Frank; Fehling, Christopg; Mietzner, Ralph; Nowak, Alexander; Dustdar, Schahram

doi:10.1142/s0218843011002250

Cited by 78 publications

(45 citation statements)

References 9 publications

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“…Optimal component distribution is discussed and analyzed by [10] in relation to virtual servers. Research conducted in [11] is of a similar nature to this paper in that it attempts to reduce costs (using a heuristic search approach is inspired on hill climbing), specifically in relation to the use of VMs from an IaaS provider with limitations around SLAs response time.…”

Section: Related Work On Optimal Deployment and Allocation Of Cloud Rmentioning

confidence: 99%

A Model for Optimising the Deployment of Cloud-hosted Application Components for Guaranteeing Multitenancy Isolation

Ochei¹,

Ejiofor²

2018

Adv. sci. technol. eng. syst. j.

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Section: Related Work On Optimal Deployment and Allocation Of Cloud Rmentioning

confidence: 99%

A Model for Optimising the Deployment of Cloud-hosted Application Components for Guaranteeing Multitenancy Isolation

Ochei¹,

Ejiofor²

2018

Adv. sci. technol. eng. syst. j.

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“…MOCCA The MOve to Clouds for Composite Applications (MOCCA) approach [39] proposes a method for migrating legacy software to a cloud environment MOCCA comes with a dedicated metamodel that covers modeling elements for representing the architecture ancl the cleployment of the legacy software. Based on these models, the deployment in a cloud environment can be derived and expressed in terms of a clustering of architectural elements and concrete implementation units that are assigned to the virtual resources of a cloud environment.…”

Section: Cloudml-ufpementioning

confidence: 99%

“…Taking existing literature [1 8], [27], [39] into consideration, we derived five activities as summarized by the migration process illustrated in Figure 2. The support offered by the selected approaches for each activity is also indicated in this figure (cf.…”

Section: Cloudifying the Good Old Java Petstorementioning

confidence: 99%

“…Starting from the above-mentioned projects and their related work, we selected teo different approaches: Blueprint [43], CAML [8], CJoudMIG [27], CloudML [23], CloudML-UFPE 1 [30], H<JP!, MOCCA [39], MULTICLAPP [3 1], RESERVOIR-ML [20], and TOSCA [10]. For each selected approach, we provide a brief description and then categorize it according to both modeling language characteristics and cloud computing characteristics.…”

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confidence: 99%

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Cloud Modeling Languages by Example

Bergmayr

Wimmer

Kappel

et al. 2014

2014 IEEE 7th International Conference on Service-Oriented Computing and Applications

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Abstract-Rece ntly, several proposals towards a cloud model· ing language have erneq;;cd. As they ad dress the diversity of cloud envimnments, il is not surprising that these modeling languages support different S(. 'Cfllll·ios. Using a by-example approach based on the sce nario of softw:uc migration, we demonstrate their represe ntational Cllpabilitics :md review them according to d!aracteristics common lo all modeling languages as weiJ as specific to the cloud computing donmin. We repor t on our findings an d present research guidelines for future efforts towards a better alignment of the different cloud modeling languages. I. I NTRODUCTIONCloud computing bas recently emerged as a new possibility how software can be made available to clients as a service. Hereby, software is hosted on a cloud environment [5] and consumable over the network by different clients. For software vendors, th. is is appealing because of low upfroot costs compared to a traditional on-premise solution as well as operational costs that scale with the provisioning and releasing of cloud resources, which are in turn offered as a service by cloud providers. Cloud service offerings range from low-level infrastructure elements, such as raw computing nodes, over higher level platforms, such as a Java execution environment on top of a cloud infrastructure, to ready-to-use software deployed on a cloud platform. Furthermore, software vendors are no longer forced to plan far ahead for resource provisioning [3] because the large-scale datacenters of today's cloud providers ensure that requested resources are available through their cloud environments. The risk of under-and over-provisioning of such resources is reduced by cloud environments due to their capabilities for elastic scalability on demand. Therefore, resources are not only provisioned as their demand increases but also released once their demand decreases (e.g. , number of user requests exceeds or falls below a defined threshold).Since cloud environments offer novel optimization opportunities (e.g., advanced scalable data persistence solutions), taking full advantage of the cloud requires that the software is prepared for such an environment However, in order to leverage the possibilities of a cloud environment, the software often needs to comply with certain restrictions that might hinder its functioning (e.g., statefuJ components in a highly scalable cloud environment). Several previous and ongoing European projects, notably REMICS [41] For each selected approach, we provide a brief description and then categorize it according to both modeling language characteristics and cloud computing characteristics. We do not claim that this set of cloud modeling language is complete and leave a systematic li terature review for future work. Rather, we foc us on the ex tensional perspective of the modeling languages by demonstrating the approaches in the setting of a cloud migration scenario. We have selected this scenario since it covers the wide spectrum of the software lifecycle. Finally, we c...

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“…Moreover, they describe the mechanisms which must be implemented by the clouds to support standard-based application deployment and management. Nevertheless, the standard efforts do not focus on getting an heterogeneous multi-cloud solution, so distributing a complex application over multiple cloud service providers is still a challenging task [5]. TOSCA and CAMP are emergent standards and they do not have official implementations yet.…”

Section: Introductionmentioning

confidence: 99%

Towards a Flexible Deployment of Multi-cloud Applications Based on TOSCA and CAMP

Carrasco

Cubo

Pimentel

2015

Communications in Computer and Information Science

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Abstract. Cloud Computing platforms offer diverse services and capabilities with own features. Hence, the provider services could be used by end users to compose a heterogeneous context of multiple cloud platforms in order to deploy their cloud applications made up of a set of modules, according to the best capabilities of the cloud providers. However, this is an ideal scenario, since the cloud platforms are being conducted in an isolated way by presenting many interoperability and portability restrictions, which complicate the integration of diverse provider services to achieve an heterogeneous deployment of multi-cloud applications. In this ongoing work, we present an approach based on model transformation to deploy multi-cloud applications by reusing standardization efforts related to the management and deployment of cloud applications. Specifically, using mechanisms specified by both standards, TOSCA and CAMP, we propose a methodology to describe the topology and distribution of modules of a cloud application and to deploy the interconnected modules over heterogeneous clouds. We illustrate our idea using a running example.

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Moving Applications to the Cloud: An Approach Based on Application Model Enrichment

Cited by 78 publications

References 9 publications

A Model for Optimising the Deployment of Cloud-hosted Application Components for Guaranteeing Multitenancy Isolation

A Model for Optimising the Deployment of Cloud-hosted Application Components for Guaranteeing Multitenancy Isolation

Cloud Modeling Languages by Example

Towards a Flexible Deployment of Multi-cloud Applications Based on TOSCA and CAMP

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