Using templates to predict execution time of scientific workflow applications in the grid

2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, CCGRID 2009

Volumn , Issue , 2009, Pages 316-323

  Nadeem, Farrukh ^a   Fahringer, Thomas ^a  

^a UNIVERSITY OF INNSBRUCK   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER ARCHITECTURE; FORECASTING;

ADVANCE RESERVATION; DYNAMIC BEHAVIORS; EXPERT USERS; GRID INFRASTRUCTURES; GRID RESOURCE; SCIENTIFIC WORKFLOWS; WORKFLOW EXECUTION; WORKFLOW STRUCTURES;

CLUSTER COMPUTING;

EID: 70349750046     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ccgrid.2009.77     Document Type: Conference Paper

References (19)

1. David A. Bacigalupo et al. An investigation into the application of different performance prediction methods to distributed enterprise applications. J. Supercomput., 34(2):93-111, 2005.
2. Dieter Theiner et al. Reduction of calibration time of distributed hydrological models by use of grid computing and nonlinear optimisation algorithms. In Proceedings of the 7th International Conference on Hydroinformatics, Sep. 2006.
3. T. Fahringer, R. Prodan, R. Duan, J. Hofer, F. Nadeem, F. Nerieri, J. Q. Stefan Podlipnig, M. Siddiqui, H.-L. Truong, A. Villazon, , and M. Wieczorek. Scientific Workflows for Grids, chapter ASKALON: A Development and Grid Computing Environment for Scientific Workflows, page 530. Workflows for e-Science. Springer, Springer Verlag, 2007.
4. Farrukh Nadeem et al. Soft benchmarks-based application performance prediction using a minimum training set. In E-SCIENCE '06: Proceedings of the Second IEEE International Conference on e-Science and Grid Computing, page 71. IEEE Computer Society, 2006.
5. R. Gibbons. A historical application profiler for use by parallel schedulers. In Job Scheduling Strategies for Parallel Processing. Springer Verlag, 1997.
6. Hui Li et al. Improving a local learning technique for queuewait time predictions. In CCGRID '06.
7. Hui Li et al. Predicting job start times on clusters. In CCGRID '04.
8. Ian Taylor et al. Distributed computing with triana on the grid: Research articles. Concurr. Comput. : Pract. Exper., 17(9), 2005.
9. Ludäscher, Bertram et al. Scientific workflow management and the kepler system. Concurrency and Computation: Practice & Experience, 18(10):1039-1065, 2006.
10. Oinn, T. et. al. Taverna: a tool for the composition and enactment of bioinformatics workflows. Bioinformatics, 20(17):3045-3054, Nov 2004.
11. P. Blaha et al. WIEN2k: An Augmented Plane Wave plus Local Orbitals Program for Calculating Crystal Properties. Institute of Physical and Theoretical Chemistry, TU Vienna, 2001.
12. R. Prodan and T. Fahringer. Overhead analysis of scientific workflows in grid environments. IEEE Trans. Parallel Distrib. Syst., 19(3):378-393, 2008.
13. J. Qin and T. Fahringer. Advanced data flow support for scientific grid workflow applications. In SC '07.
14. S. Felix. et al. Performance, Scalability and Quality of the Meteorological Grid Workflow MeteoAG. In 2nd Austrian Grid Symposium, Innsbruck, Austria, Sep. 2006.
15. The Austrian Grid Consortium. http://www.austriangrid.at.
16. Tristan Glatard et al. A probabilistic model to analyse workflow performance on production grids. In CCGRID, pages 510-517, 2008.
17. Valerie Taylor et al. Using kernel couplings to predict parallel application performance. In HPDC '02.
18. Warren Smith et al. Predicting application run times with historical information. J. Parallel and Distrib. Comput., 2004.
19. A. Wombacher and M. Rozie. Piloting an empirical study on measures forworkflow similarity. In IEEE SCC, pages 94-102, 2006.