7 Conclusion
In grid environments, the grid scheduling technique is more complex than the conventional ones in high performance computing system, and grid scheduling is one of the major factors that would affect the grid performance. In order to optimize grid scheduling, we have to consider the various factors. By combining the analysis and prediction methods that are of different principles and approaches, we would be able to make comprehensive decisions on different scenarios and provide reference for scheduling optimization. In this paper, a method of fuzzy multi-attribute group decision-making is proposed, which introduces fuzzy set and its operations into decision-making process, and reflects a group or collective ranking of alternatives based on the individual preferences of those alternatives. The flexible selection models heighten the expressive force and adaptability greatly. The experiments show that the grid scheduling with this method has high performance.
It should be pointed out that the decision-making approach in this paper is built on the compensability between the decision attributes. But in some cases, the compensability between the decision attributes is conditional, and even non-compensable. Therefore, the other comprehensive decision-making approaches are needed for these features. These approaches will be our further research focus.
This paper is supported by ChinaGrid project funded by Ministry of Education of China, National Science Foundation under grant 90412010, and China CNGI project under grant CNGI-04-15-7A.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Kapadia N, Fortes J, Brodley C (1999) Predictive application-performance modeling in a computational Grid environment. In: Proceedings of the 8th International Conference on High Performance Distributed Computing, 47–54
Bacigalupo D, Jarvis S, He L, Nudd G (2004) An investigation into the application of different performance prediction techniques to e-Commerce applications. In: Proceedings of the 18th International Conference on Parallel and Distributed Processing
NWS Project. http://nws.cs.ucsb.edu/
Zhu H, Parashar M, Yang J, Zhang Y, Rao S, Hariri S (2003) Self-adapting, self-optimizing runtime management of Grid applications using PRAGMA. In: Proceedings of the International Conference on Parallel and Distributed Processing Symposium
Alkindi A, Kerbyson D, Papaefstathiou E, Nudd G (2000) Run-time optimisation using dynamic performance prediction. In: Proceedings of the International Conference on High Performance Computing and Networking, 280–289
GrADS Project. http://www.hipersoft.rice.edu/grads/
Kacprzyk J, Fedrizzi M, Nurmi H (1992) Group decision making and consensus under fuzzy preferences and fuzzy majority. Fuzzy Sets and Systems, 49(1):21–31
Ishikawa A, Amagasa M, Shiga T, Tomizawa G, Tatsuta R, Mieno H (1993) The max-min Delphi method and fuzzy Delphi method via fuzzy integration. Fuzzy Sets and Systems, 55(3):241–253
Bardossy A, Duckstein L, Bogardi I (1993) Combination of fuzzy numbers representing expert opinions. Fuzzy Sets and Systems, 57(2):173–181
Hsu H, Chen C (1996) Aggregation of fuzzy opinions under group decision making. Fuzzy Sets and Systems, 79(3):279–285
Cheng C (1999) A simple fuzzy group decision making method. In: Proceedings of the 10th International Conference on Fuzzy Systems, 910–915
Lan J, Xu Y, Liu J (2003) Multiple attributes group decision making under fuzzy environment. In: Proceedings of the International Conference on Systems, Man and Cybernetics, 4986–4991
Prodanovic P, Simonovic S (2003) Fuzzy compromise programming for group decision making. In: Proceedings of the International Conference on Systems, Man and Cybernetics, 358–365
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berling Heidelberg
About this chapter
Cite this chapter
Huang, J., Jin, H., Xie, X., Zhao, J. (2006). A Grid Scheduling Optimization Strategy Based on Fuzzy Multi-Attribute Group Decision-Making. In: Last, M., Szczepaniak, P.S., Volkovich, Z., Kandel, A. (eds) Advances in Web Intelligence and Data Mining. Studies in Computational Intelligence, vol 23. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-33880-2_10
Download citation
DOI: https://doi.org/10.1007/3-540-33880-2_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33879-6
Online ISBN: 978-3-540-33880-2
eBook Packages: EngineeringEngineering (R0)