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Two Risk-aware Resource Brokering Strategies in Grid Computing:Broker-driven vs. User-driven Methods

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  • Junseok Hwang

    ()

  • Jihyoun Park

    ()

  • Jorn Altmann

    ()
    (Technology Management, Economics, and Policy Program (TEMEP), Seoul National University)

Abstract

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.

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Bibliographic Info

Paper provided by Seoul National University; Technology Management, Economics, and Policy Program (TEMEP) in its series TEMEP Discussion Papers with number 201063.

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Length: 17 pages
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
Handle: RePEc:snv:dp2009:201063

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Keywords: Grid Computing; Risk Management; Self-Insurance; Risk-Performance Preference Specification;

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  1. Tesfatsion, Leigh, 2006. "Agent-Based Computational Economics: A Constructive Approach to Economic Theory," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 16, pages 831-880 Elsevier.
  2. 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 6.
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