An Approach to Support Automated Deployment of Applications on Heterogeneous Cloud-HPC Infrastructures

Nitto, Elisabetta Di; Gorroñogoitia, Jesús; Kumara, Indika; Meditskos, Georgios; Radolović, Dragan; Sivalingam, Karthee; González, Román Sosa

doi:10.1109/synasc51798.2020.00031

Cited by 11 publications

(12 citation statements)

References 20 publications

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“…Our approach is firmly grounded on model-driven engineering [2], and has been inspired by our previous works on classical and cloud service engineering [11,13]. Notably, the QSOA methodology has benefited from ongoing works on heterogeneous application engineering [4]. Figure 2 provides a graphical overview of the proposed methodology for engineering QSOA applications.…”

Section: Model-driven Quantum Soa Application Engineering Methodologymentioning

confidence: 99%

“…Knowledgebase consists of versioned-controlled ontologies or knowledge graphs of abstract blueprints to support discovery, matchmaking, reuse, composition, validation, and optimization of concrete blueprints. To implement the Knowledgebase, the existing ontologies can be adopted, for example, IaC ontologies [4] and service ontologies [8]. Ontologies enable representing both structural and semantics relationships of blueprints in an unambiguous manner, promoting reusability and interoperability.…”

Section: Model-driven Quantum Soa Application Engineering Methodologymentioning

confidence: 99%

“…Such models need to be further refined and transformed into executable platform-specific models and code yet hardware-agnostic models/codes. For example, the deployment architecture models can be transformed into IaC programs such as TOSCA and Ansible [4,17]. The service variants can be generated from the service implementation artifacts retrieved from the Artifact Repository using the service contracts.…”

Section: Model-driven Quantum Soa Application Engineering Methodologymentioning

confidence: 99%

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QSOC: Quantum Service-Oriented Computing

Kumara¹,

Heuvel²,

Tamburri³

2021

Preprint

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Quantum computing is quickly turning from a promise to a reality, witnessing the launch of several cloud-based, general-purpose offerings, and IDEs. Unfortunately, however, existing solutions typically implicitly assume intimate knowledge about quantum computing concepts and operators. This paper introduces Quantum Service-Oriented Computing (QSOC), including a model-driven methodology to allow enterprise DevOps teams to compose, configure and operate enterprise applications without intimate knowledge on the underlying quantum infrastructure, advocating knowledge reuse, separation of concerns, resource optimization, and mixed quantum-& conventional QSOC applications.

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Section: Model-driven Quantum Soa Application Engineering Methodologymentioning

confidence: 99%

Section: Model-driven Quantum Soa Application Engineering Methodologymentioning

confidence: 99%

Section: Model-driven Quantum Soa Application Engineering Methodologymentioning

confidence: 99%

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QSOC: Quantum Service-Oriented Computing

Kumara¹,

Heuvel²,

Tamburri³

2021

Preprint

Self Cite

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“…The SODALITE@RT currently supports five key types of infrastructures: edge (Kubernetes 21 ), private cloud (OpenStack 22 and Kubernetes), public cloud (AWS), federated cloud (EGI OpenStack 23 ), and HPC (TORQUE 24 and SLURM 25 ). The HPC support was partially presented in a previous publication [39]. The examples for orchestrating applications on each type of these infrastructures can be found in our GitHub repository.…”

Section: Sodalite@rt Prototype Implementationmentioning

confidence: 99%

“…In addition to the runtime environment, the SODALITE project also includes a development environment, implemented as an Eclipse plugin to support authoring defect-free TOSCA blueprints and Ansible scripts. We have presented our development environment and its capabilities in our previous publications [39][40][41][42][43].…”

Section: Sodalite@rt Prototype Implementationmentioning

confidence: 99%

SODALITE@RT: Orchestrating Applications on Cloud-Edge Infrastructures

Kumara

Mundt²,

Tokmakov

et al. 2021

J Grid Computing

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IoT-based applications need to be dynamically orchestrated on cloud-edge infrastructures for reasons such as performance, regulations, or cost. In this context, a crucial problem is facilitating the work of DevOps teams in deploying, monitoring, and managing such applications by providing necessary tools and platforms. The SODALITE@RT open-source framework aims at addressing this scenario. In this paper, we present the main features of the SODALITE@RT: modeling of cloud-edge resources and applications using open standards and infrastructural code, and automated deployment, monitoring, and management of the applications in the target infrastructures based on such models. The capabilities of the SODALITE@RT are demonstrated through a relevant case study.

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