WS-PGRADE/gUSE Generic DCI Gateway Framework for a Large Variety of User Communities

Kacsuk, Péter; Farkas, Zoltán; Kozlovszky, Miklós; Hermann, Gabor; Balaskó, Ákos; Karóczkai, Krisztián; Márton, István

doi:10.1007/s10723-012-9240-5

Cited by 174 publications

(158 citation statements)

References 23 publications

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“…Our future work will address the evaluation of the proposed algorithms in the workflow execution environment [14] of the SHIWA project [23], and the examination of the applicability of this approach to Cloud-based DCIs. To this end we will investigate how virtualization affects the execution time of previously developed workflow applications, and how comparisons can be made among Grid and Cloud resources for multi-job allocations.…”

Section: Simulation Resultsmentioning

confidence: 99%

On Efficiency of Multi-job Grid Allocation Based on Statistical Trace Data

et al. 2013

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The ever growing number of computationintensive applications calls for utilizing large-scale, potentially interoperable distributed infrastructures. Nowadays, such distributed systems enable the management of heterogeneous scientific workflows of considerable sizes, where job scheduling and resource management is a crucial issue. In this paper we focus on the challenges of scheduling parameter sweep applications, a specific and commonly used type of workflows where ordering of job executions is irrelevant. A parameter sweep has a large set of independent job instances, called a multi-job, submitted for execution in a single step. In order to cope with the high uncertainty and unpredictable load of resources, and the si- multaneous submissions of multi-job instances, we propose a statistics-based brokering approach for allocating jobs to resources so that the makespan is minimised. Earlier studies claim that users' predictions on job runtime are inaccurate and unusable for scheduling. Our aim is to examine, whether statistical trace data for the same purpose is efficient compared to randomized allocation.

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Section: Simulation Resultsmentioning

confidence: 99%

On Efficiency of Multi-job Grid Allocation Based on Statistical Trace Data

et al. 2013

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“…For some specific frameworks used in science gateways such as gUSE/WS-PGRADE [2,3] procedures exists to extend underlying databases, functionalities and portlets consequently upgrading the gUSE/WS-PGRADE version. Science gateways like the AMC Neuroscience Gateway [4], the VisIVO Gateway [5] and the Statistical Seismology Gateway [6], but also others, undergo this process regularly.…”

Section: Related Workmentioning

confidence: 99%

“…The Grid User Support Environment (gUSE) enables distributed computing over a range of supported middlewares and backend execution environments [2]. The frontend is driven by an Apache Tomcat (version 7.0.55) application server with Liferay (version 6.2) already fully incorporated.…”

Section: Guse/ws-pgradementioning

confidence: 99%

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Maintaining a Science Gateway - Lessons Learned from MoSGrid

Zimmermann¹,

Grunzke²,

Krüger³

2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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“…SCI-BUS is based on the WS-PGrade/gUSE technology [10] and its aim is to support a large number of different communities in building portals, implementing execution logic based on workflows and in accessing diverse Distributed Computing Infrastructures(DCI).…”

Section: The Sci-bus Projectmentioning

confidence: 99%

Extending the Sheba Propagation Model to Reduce Parameter-Related Uncertainties

Gabriele¹,

Brian²,

Eamonn³

et al. 2013

csci

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Heliophysics is the branch of physics that investigates the interactions and correlation of different events across the Solar System. The mathematical models that describe and predict how physical events move across the solar system (ie. Propagation Models) are of great relevance. These models depend on parameters that users must set, hence the ability to correctly set the values is key to reliable simulations. Traditionally, parameter values can be inferred from data either at the source (the Sun) or arrival point (the target) or can be extrapolated from common knowledge of the event under investigation. Another way of setting parameters for Propagation Models is proposed here: instead of guessing a priori parameters from scientific data or common knowledge, the model is executed as a parameter-sweep job and selects a posteriori the parameters that yield results most compatible with the event data. In either case (a priori and a posteriori), the correct use of Propagation Models requires information to either select the parameters, validate the results, or both. In order to do so, it is necessary to access sources of information. For this task, the HELIO project proves very effective as it offers the most comprehensive integrated information system in this domain and provides access and coordination to services to mine and analyze data. HELIO also provides a Propagation Model called SHEBA, the extension of which is currently being developed within the SCI-BUS project (a coordinated effort for the development of a framework capable of offering to science gateways seamless access to major computing and data infrastructures).

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WS-PGRADE/gUSE Generic DCI Gateway Framework for a Large Variety of User Communities

Cited by 174 publications

References 23 publications

On Efficiency of Multi-job Grid Allocation Based on Statistical Trace Data

On Efficiency of Multi-job Grid Allocation Based on Statistical Trace Data

Maintaining a Science Gateway - Lessons Learned from MoSGrid

Extending the Sheba Propagation Model to Reduce Parameter-Related Uncertainties

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