Intelligent selection of fault tolerance techniques on the grid

Proceedings - e-Science 2007, 3rd IEEE International Conference on e-Science and Grid Computing

Volumn , Issue , 2007, Pages 69-76

  Vanderster, Daniel C ^a   Dimopoulos, Nikitas J ^a   Sobie, Randall J ^a  

^a UNIVERSITY OF VICTORIA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL METHODS; COMPUTER SYSTEMS; ERRORS; ESTIMATION; FAULT TOLERANCE; GRID COMPUTING; INTELLIGENT SYSTEMS; LEAD; MULTITASKING; QUALITY ASSURANCE;

CHECK-POINTING; COMPUTATIONAL GRIDS; E SCIENCES; FAULT TOLERANCE TECHNIQUES; GLOBAL UTILITY; INTERNATIONAL CONFERENCES; LOW PRIORITY (LP); RELIANCE (CO); REPLICATION TECHNIQUES; RESOURCE COSTS; RESPONSE TIME; RUN TIME; SELECTION STRATEGIES; SIMULATION RESULTS;

RELIABILITY;

EID: 44949174872     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/E-SCIENCE.2007.45     Document Type: Conference Paper

References (21)

1. J. H. Abawajy. Fault-Tolerant Scheduling Policy for Grid Computing Systems. IPDPS'04, pages 238-244.
2. C. Anglano and M. Canonico. Fault-Tolerant Scheduling for Bag-of-Tasks Grid Applications. In EuroGrid2005.
3. H. Casanova. Simgrid: A Toolkit for the Simulation of Application Scheduling. In CCGRID'01, pages 430-437.
4. K. L. et al. Reliability-Aware Resource Management for Computational Grid/Cluster Environments. In Grid2005.
5. I. Foster and C. Kesselman, editors. The Grid 2: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, second edition, 11 2003.
6. Linked Environments for Atmospheric Discover (LEAD) Portal, 2007. [Online]. Available: http://portal.leadproject.org/.
7. R. Medeiros, W. Cirne, F. Brasileiro, and J. Sauvé. Faults in Grids: Why are they so bad and What can be done about it? In Grid2003, pages 18-24.
8. D. Nurmi, J. Brevik, and R. Wolski. Modeling Machine Availability in Enterprise and Wide-Area Distributed Computing Environments. In Euro-Par 2005, pages 432-441.
9. R. Parra-Hernandez and N. Dimopoulos. Heuristic Approaches for Solving the Multidimensional Knapsack Problem (MKP). WSEAS Transactions on Systems, pages 248-253, 4 2002.
10. R. Parra-Hernandez and N. Dimopoulos. A New Heuristic for Solving the Multi-choice Multidimensional Knapsack Problem. IEEE Transactions on Systems, Man, and Cybernetics, 35(5):708-717, 2005.
11. R. Parra-Hernandez, D. Vanderster, and N. J. Dimopoulos. Resource Management and Knapsack Formulations on the Grid. In Grid2004, pages 94-101.
12. H. Pirkul. A Heuristic Solution Procedure for the Multiconstraint Zero-One Knapsack Problem. Naval Research Logistics, pages 161-172, 1987.
13. The Open Science Grid Project, 2007. [Online]. Available: http://www.opensciencegrid.org/.
14. The VGrADS Project, 2007. [Online]. Available: http://vgrads.rice.edu/.
15. D. C. Vanderster and N. J. Dimopoulos. Sensitivity Analysis of Knapsack-based Task Scheduling on the Grid. In ICS 2006, pages 317-323.
16. D. C. Vanderster, N. J. Dimopoulos, R. Parra-Hernandez, and R. J. Sobie. Evaluation of Knapsack-based Scheduling using the NPACI JOBLOG. In HPCS 2006.
17. D. C. Vanderster, N. J. Dimopoulos, and R. J. Sobie. Metascheduling Multiple Resource Types using the MMKP. In Grid2006, pages 231-237.
18. R. Wolski. Experiences with Predicting Resource Performance On-line in Computational Grid Settings. SIGMETRICS Perform. Eval. Rev., 30(4):41-49, 2003.
19. R. Wolski, N. T. Spring, and J. Hayes. The Network Weather Service: A Distributed Resource Performance Forecasting Service for Metacomputing. Future Generation Computer Systems, 15(5-6):757-768, 1999.
20. Worldwide LHC Computing Grid Project, 2007. [Online]. Available: http://1cg.web.cern.ch/LCG/.
21. J. Yu and R. Buyya. A Taxonomy of Workflow Management Systems for Grid Computing. Journal of Grid Computing, 3:171-200, 2006.