P‐GRADE portal family for grid infrastructures

Kacsuk, Péter

doi:10.1002/cpe.1654

Cited by 88 publications

(62 citation statements)

References 23 publications

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“…gUSE (Grid and Cloud User Support Environment) [7] is an open source scientific gateway framework providing users with easy access to cloud and grid infrastructures. gUSE provides with WS-PGRADE, a Liferay based portal to create and execute scientific workflows in various Distributed Computing Infrastructures (DCIs) including clusters, grids and clouds.…”

Section: Ws-pgrade/gusementioning

confidence: 99%

“…These workflows can be used for large scale experimentation and can be run on distributed computing infrastructures to provide fast and efficient program execution. Workflow systems are also frequently combined with science gateway frameworks [28], such as WS-PGRADE/gUSE [7], to provide a user friendly execution environment for domain scientist end-users. The integration of Big Data processing frameworks and solutions, such as MapReduce and Hadoop, to science gateways and workflows could take the burden of setting-up and managing these computation environments from domain scientists and facilitate the wider take-up of such technologies.…”

Section: Introductionmentioning

confidence: 99%

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Extending Science Gateway Frameworks to Support Big Data Applications in the Cloud

et al. 2016

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Cloud computing offers massive scalability and elasticity required by many scientific and commercial applications. Combining the computational and data handling capabilities of clouds with parallel processing also has the potential to tackle Big Data problems efficiently. Science gateway frameworks and workflow systems enable application developers to implement complex applications and make these available for end-users via simple graphical user interfaces. The integration of such frameworks with Big Data processing tools on the cloud opens new opportunities for application developers. This paper investigates how workflow systems and science gateways can be extended with Big Data processing capabilities. A generic approach based on infrastructure aware workflows is suggested and a proof of concept is implemented based on the WS-PGRADE/gUSE science gateway framework and its integration with the Hadoop parallel data processing solution based on the MapReduce paradigm in the cloud. The provided analysis demonstrates that the methods described to

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Section: Ws-pgrade/gusementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Extending Science Gateway Frameworks to Support Big Data Applications in the Cloud

et al. 2016

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“…One of the main goals of the GreenLand platform is to provide workflows that could be reused in other applications, such as Pegasus, Taverna, PGRADE [15], etc. This could be achieved by using the SHIWA (SHaring Interoperable Workflows for large-scale scientific simulations on Available DCIs) [16] platform that offers interoperability services in order to standardize the workflow development and portability.…”

Section: Related Workmentioning

confidence: 99%

“…All these processes are represented within the GreenLand platform as acyclic graphs, composed from basic operators, Web services and sub-graphs [15].…”

Section: Introductionmentioning

confidence: 99%

Mathematical Modeling of Distributed Image Processing Algorithms

Colceriu¹,

Mihon²,

Minculescu³

et al. 2013

SpecialIssue

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Abstract-Satellite images play an important role in developing Geographical Information System software applications that prove to be useful for different Earth Science phenomena analysis. Accurate results are obtained from high resolution images, or by applying the same algorithm multiple times over a specific input data set. In both cases the data volume that needs to be processed is large, and usually involves distributed infrastructures. In order for non-technical users to use these algorithms, they should be described in a flexible manner, using workflow structure models. This paper highlights the main achievements within the GreenLand platform, regarding scheduling, executing, and monitoring the Grid processes. Its development is based on simple, but powerful, notion of mathematical directed acyclic graphs that are used in parallel and distributed executions over the Grid infrastructure.

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“…We therefore call these languages data-flow oriented. Languages like AGWL [15], GWENDIA [25], gUSE [19], SCUFL [23] and Triana Taskgraph [31] belong to the category of data-flow oriented scientific workflow languages.…”

Section: Introductionmentioning

confidence: 99%

Fine-Grain Interoperability of Scientific Workflows in Distributed Computing Infrastructures

et al. 2013

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Today there exist a wide variety of scientific workflow management systems, each designed to fulfill the needs of a certain scientific community. Unfortunately, once a workflow application has been designed in one particular system it becomes very hard to share it with users working with different systems. Portability of workflows and interoperability between current systems barely exists. In this work, we present the fine-grained interoperability solution proposed in the SHIWA European project that brings together four representative European workflow systems: ASKALON, MOTEUR, WS-PGRADE, and Triana. The proposed interoperability is realised at two levels of abstraction: abstract and concrete. At the abstract level, we propose a generic Interoperable Workflow Intermediate Representation (IWIR) that can be used as a common bridge for translating workflows between different languages independent of the underlying distributed computing infrastructure. At the concrete level, we propose a bundling technique that aggregates the abstract IWIR representation and concrete task representations to enable workflow instantiation, execution and scheduling. We illustrate case studies using two real-workflow applications designed in a native environment and then translated and executed by a foreign workflow system in a foreign distributed computing infrastructure.

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P‐GRADE portal family for grid infrastructures

Cited by 88 publications

References 23 publications

Extending Science Gateway Frameworks to Support Big Data Applications in the Cloud

Extending Science Gateway Frameworks to Support Big Data Applications in the Cloud

Mathematical Modeling of Distributed Image Processing Algorithms

Fine-Grain Interoperability of Scientific Workflows in Distributed Computing Infrastructures

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