Two Risk-aware Resource Brokering Strategies in Grid Computing:Broker-driven vs. User-driven Methods
Grid computing evolves toward an open computing environment, which is characterized by highly diversified resource providers and systems. As the control of each computing resource becomes difficult, the security of users¡¯ job is often threatened by various risks occurred at individual resources in the network. This paper proposes two risk-aware resource brokering strategies: self-insurance and risk-performance preference specification. The former is a broker-driven method and the latter a user-driven method. Two mechanisms are analyzed through simulations. The simulation results show that both methods are effective for increasing the market size and reducing risks, but the user-driven technique is more cost-efficient.
|Date of creation:||Mar 2010|
|Date of revision:||Mar 2010|
|Publication status:||Published in ICISTM 2010, International Conference on Information Systems, Technology and Management, Bangkok, Thailand, 2010|
|Contact details of provider:|| Postal: 599 Gwanak-Ro, Gwanak-Gu, Seoul 151-744|
Web page: http://temep.snu.ac.kr/
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- Robert Tobias & Carole Hofmann, 2004. "Evaluation of free Java-libraries for social-scientific agent based simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 7(1), pages 1-6.
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Handbook of Computational Economics,
in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 16, pages 831-880
- Leigh Tesfatsion, 2006. "Agent-Based Computational Economics: A Constructive Approach to Economic Theory," Computing in Economics and Finance 2006 527, Society for Computational Economics.
- Tesfatsion, Leigh, 2006. "Agent-Based Computational Economics: A Constructive Approach to Economic Theory," Staff General Research Papers Archive 12514, Iowa State University, Department of Economics.
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