Adaptation to dynamic resource availability in ad-hoc grids through a learning mechanism

Proceedings - 2008 IEEE 11th International Conference on Computational Science and Engineering, CSE 2008

Volumn , Issue , 2008, Pages 171-178

  Pourebrahimi, Behnaz ^a   Bertels, Koen ^a  

^a DELFT UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

ECONOMICS; EDUCATION; LEARNING ALGORITHMS; LEARNING SYSTEMS; MANAGEMENT; MECHANISMS; PLANNING; REINFORCEMENT; RESOURCE ALLOCATION; TECHNOLOGY;

BIDDING STRATEGIES; COMPUTATIONAL SCIENCE AND ENGINEERING; DYNAMIC CONDITIONS; DYNAMIC NETWORKS; GLOBAL INFORMATIONS; INTERNATIONAL CONFERENCES; LEARNING MECHANISMS; MARKET-BASED RESOURCE ALLOCATION; PRICING STRATEGIES; RESOURCE AVAILABILITIES; VARIATIONS OF;

AD HOC NETWORKS;

EID: 51849144163     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CSE.2008.43     Document Type: Conference Paper

References (11)

1. A. J. Bagnall and I. E. Toft. Zero intelligence plus and gjerstad-dickhaut agents for sealed bid auctions. In Workshop on Trading Agent Design and Analysis, part of international conference on autonomous agents and multiagent systems (AAMAS-2004), pages 59-64, 2004.
2. R. Buyya, D. Abramson, J. Giddy, and H. Stockinger. Economic models for resource management and scheduling in grid computing. Concurrency and Computation: Practice and Experience, 14(13-15):1507-1542, 2002.
3. S.-W. Cheng, D. Garlan, B. Schmerl, P. Steenkiste, and N. Hu. Software architecture-based adaptation for grid computing. In Proceedings of the 11th IEEE Int. Symposium on High Performance Distributed Computing, page 389, Washington, DC, USA, 2002. IEEE Computer Society.
4. D. Cliff and J. Bruten. Less than human: Simple adaptive trading agents for cda markets. Technical Report HPL-97-9 I, Hewlett Packard Labs, Bristol, England, 1997.
5. D. Gode and S. Sunder. Allocative efficiency of markets with zero-intelligence traders: Market as a partial substitute for individual rationality. The Journal of Political Economy, 101(1):119-137, 1993.
6. A. Gounaris, N. W. Paton, R. Sakellariou, A. A. A. Fernandes, J. Smith, and P. Watson. Practical adaptation to changing resources in grid query processing. In ICDE '06: Proceedings of the 22nd Int. Conf. on Data Engineering, page 165, USA, 2006. IEEE Computer Society.
7. C. Lee and J. Suzuki. An immunologically-inspired autonomic framework for adaptive and survivable network applications. In ACM Transactions on Autonomous and Adaptive Systems, Special Issue on Autonomic Communications, 2007.
8. M. Preist C., Van Tol. Adaptive agents in a persistent shout double auction. In Proc. of 1st International Conference on the Internet Computing and Economics, pages 11-17, 1998.
9. K. Shirose, S. Matsuoka, H. Nakada, and H. Ogawa. Autonomous configuration of grid monitoring systems. In Proceedings of the 2004 Symposium on Applications and the Internet-Workshops, page 651, USA, 2004. IEEE Computer Society.
10. R. Sutton and A. Barto. Reinforcement Learning: An Introduction. MIT Press, 1998.
11. R. Wolski, J. Brevik, J. S. Plank, and T. Bryan. Grid resource allocation and control using computational economies. In F. Berman, G. Fox, and A. Hey, editors, Grid Computing: Making The Global Infrastructure a Reality. John Wiley & Sons, 2003.