Grid Resource Discovery Strategies Base on Multi-Economic Agent

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The paper presents a market oriented resource allocation strategy for grid resource. The proposed model uses the utility functions for calculating the utility of a resource allocation. This paper is target to solve above issues by using utility-based optimization scheme. We firstly point out the factors that influence the resources’ prices; then make out the trading flow for resource consumer agents and provider agents. By doing these, the two trading agents can decide their price due to the dynamic changes of the Grid environment without any manmade interferences. Total user benefit of the computational grid is maximized when the equilibrium prices are obtained through the consumer’s market optimization and provider’s market optimization. The economic model is the basis of an iterative algorithm that, given a finite set of requests, is used to perform optimal resource allocation.

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1100-1108

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August 2010

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© 2010 Trans Tech Publications Ltd. All Rights Reserved

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[1] I. Foster and C. Kesselman, The Grid: Blueprint for a New Computing Infrastructure, Morgan Kaufmann, (1999).

Google Scholar

[2] H. Casanova, G. Obertelli, F. Berman et al. The AppLeS parameter sweep template: User-level middleware for the grid. In: Proc. of Super Computing, Springer Verlag, 2000, 75~76.

DOI: 10.1109/sc.2000.10061

Google Scholar

[3] The Gridbus Project: http: /www. gridbus. org.

Google Scholar

[4] F. Kelly, A. Maulloo, and D. Tan. Rate control for communication networks: shadow prices, proportional fairness and stability.J. of Operational Res. Soc., 49(3): 237~252, (1998).

DOI: 10.1038/sj.jors.2600523

Google Scholar

[5] Jun. Xie, Ming Chen. A Study and Design of Game Theory-based Resource Allocation for Grids. Journal of Computational Information Systems 3: 6 (2007) 2375-2381.

Google Scholar

[6] Richard J. La and V. Anantharam, Utility-based rate control in the Internet for elastic traffic, IEEE/ACM Trans. On Networking, vol. 10, no. 2, p.272~286, (2002).

DOI: 10.1109/90.993307

Google Scholar

[7] Jonathan Bredin, Rajiv T. Maheswaran, Cagri Imer, A Game-thoryetic Formaulation of Multi-Agent Resource Allocation. In Proceeding of the Fourth International Conference on Autonomous Agents, Barcelona, May, (2000).

DOI: 10.1145/336595.337525

Google Scholar

[8] Li yayuan, Li Chunlin, A distributed QoS-Aware multicast routing protocol, Acta Information, Springer-Verlag Heidelberg, Vol 40/3, p.211~233, November (2003).

DOI: 10.1007/s00236-003-0123-x

Google Scholar

[9] O. Ercetin, L. Tassiulas, Markret based Resource Allocation for Content Delivery in the Internet, IEEE Trans. On Computers, Vol 52/12, Dec (2003).

DOI: 10.1109/tc.2003.1252853

Google Scholar

[10] T. Eymann, M. Reinicke, O. Ardaiz, Decentralized Resource Allocation in Appication Layer Networks, CCGrid2003, May, 12th-15th, 2003. Tokyo, Japan.

Google Scholar

[11] Scott Jordan, Pricing of Buffer and Bandwidth in a Reservation-Based QoS Arcitecture, IEEE International Conference on Communication, ICC'03, Ancourage, Alaska, May 2003, pp.1521-1525.

DOI: 10.1109/icc.2003.1203857

Google Scholar