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A novel graph based approach for automatic composition of high quality grid workflows
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High Performance Distributed Computing archive
Proceedings of the 18th ACM international symposium on High performance distributed computing table of contents
Garching, Germany
SESSION: Workflow and dataflow applications table of contents
Pages 167-176  
Year of Publication: 2009
ISBN:978-1-60558-587-1
Authors
Jun Qin  University of Innsbruck, Innsbruck, Austria
Thomas Fahringer  University of Innsbruck, Innsbruck, Austria
Radu Prodan  University of Innsbruck, Innsbruck, Austria
Sponsors
ACM: Association for Computing Machinery
SIGARCH: ACM Special Interest Group on Computer Architecture
Publisher
ACM  New York, NY, USA
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ABSTRACT

The workflow paradigm is one of the most important programming models for the Grid. The composition of Grid workflows has been widely studied in the Grid community. However, there is still a lack of a general and efficient approach for automatic composition of Grid workflows. In this paper, we present a STRIPS (Stanford Research Institute Problem Solver) based formal definition of the Grid workflow composition problem, followed by a novel graph based algorithm for automatic composition of high quality (portable, fault tolerant and optimized) Grid workflows. Our algorithm searches for semantic descriptions of workflow activities, i.e., Activity Functions (AFs), defined by ontologies and composes them into Grid workflows using AF Data Dependence (ADD) graphs. The composition process consists of three phases: ADD graph creation, workflow extraction, and workflow optimization. The worst case complexity of our algorithm is quadratic in the number of AFs. An extension of our algorithm to compose Grid workflows with branches and loops is also presented. Experimental results illustrate the effectiveness and efficiency of our approach: (i) the measured worst case execution time of our algorithm further proofs the analyzed time complexity; (ii) the composition of the real world meteorology Grid workflow application MeteoAG with our algorithm takes approximate half a second; and (iii) the execution time of the MeteoAG workflow when running on the Austrian Grid is reduced by up to 25% and the speedup is increased by up to 2.24 by applying our workflow optimization techniques.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Jun Qin: colleagues
Thomas Fahringer: colleagues
Radu Prodan: colleagues