Galaxy Workflow Integration on Garuda Grid

Karuna,; Mangala, N.; Ch, Janaki; Shashi, S; Subrata, C.

doi:10.1109/wetice.2012.51

Cited by 3 publications

(3 citation statements)

References 8 publications

(6 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The scheduling algorithms can be based on the queue techniques in a publish/subscribe model with different strategies such as First In First Out (FIFO), adaptive and so on. SWfMSs such as Swift [135], Chiron [102], and Galaxy [79] exploit dynamic scheduling algorithms.…”

Section: Dynamic Schedulingmentioning

confidence: 99%

“…In the WEP generation layer, Galaxy manages the dependencies between each activity for workflow parallelization. In the WEP execution layer, Galaxy generates concrete tasks for each activity, puts the tasks in a queue to be submitted, and monitors the task status (in queue, running or completion) [79]. Through this mechanism, Galaxy exploits dynamic scheduling to dispatch executable tasks.…”

Section: Galaxymentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Data-Intensive Scientific Workflow Management

et al. 2015

View full text Add to dashboard Cite

International audienceNowadays, more and more computer-based scientific experiments need to handle massive amounts of data. Their data processing consists of multiple computational steps and dependencies within them. A data-intensive scientific workflow is useful for modeling such pro- cess. Since the sequential execution of data-intensive scientific workflows may take much time, Scientific Workflow Management Systems (SWfMSs) should enable the parallel execution of data-intensive scientific workflows and exploit the resources distributed in different infrastruc- tures such as grid and cloud. This paper provides a survey of data-intensive scientific workflow management in SWfMSs and their parallelization techniques. Based on a SWfMS functional ar- chitecture, we give a comparative analysis of the existing solutions. Finally, we identify research issues for improving the execution of data-intensive scientific workflows in a multisite cloud

show abstract

Section: Dynamic Schedulingmentioning

confidence: 99%

Section: Galaxymentioning

confidence: 99%

A Survey of Data-Intensive Scientific Workflow Management

et al. 2015

View full text Add to dashboard Cite

show abstract

“…GARUDA is facilitating the OSDD applications [20,21] grid computing also enhanced the collaboration between bio-and chemoinformatics researchers. Figure 8 shows the compute-data intensive phases of drug discovery, where grid GARUDA is utilized.…”

Section: Osddmentioning

confidence: 99%

GARUDA: Pan-Indian distributed e-infrastructure for compute-data intensive collaborative science

Mangala

Rao

Chattopadhyay

et al. 2013

CSIT

View full text Add to dashboard Cite

GARUDA is a nation-wide grid of computational nodes, mass storage and scientific instruments with an aim to provide technological advancements required to enable compute-data intensive, collaborative applications for the twenty-first century. From a Proof-of-Concept, the GARUDA has evolved to an operational grid, aggregating nearly 70TF-15TB compute-storage power, via high-speed National Knowledge Network and hosts a stack of middleware and tools to enable hundreds of users from diverse communities like life science, earth science, computer aided engineering, material science, etc. Evolution and confluence of research and technologies has led to the maturity of GARUDA grid: there have been addition of several hundred CPUs, large data stores, standardization of grid middleware, research on interoperability between grids and participation from varied application communities that have made significant impact to GARUDA. The GARUDA partner institutes are using this e-infrastructure to grid enable applications of societal and national importance. The authors in this paper present the manner of building a nation-wide operational grid and its evolution, its deliverables, architecture and applications.

show abstract

Adapting Medical Image Processing Tasks to a Scalable Scientific Workflow System

Hamidian

Rana

et al. 2014

2014 IEEE World Congress on Services

View full text Add to dashboard Cite

Galaxy Workflow Integration on Garuda Grid

Cited by 3 publications

References 8 publications

A Survey of Data-Intensive Scientific Workflow Management

A Survey of Data-Intensive Scientific Workflow Management

GARUDA: Pan-Indian distributed e-infrastructure for compute-data intensive collaborative science

Adapting Medical Image Processing Tasks to a Scalable Scientific Workflow System

Contact Info

Product

Resources

About