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

Author

Listed:
  • 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.

Suggested Citation

  • Junseok Hwang & Jihyoun Park & Jorn Altmann, 2010. "Two Risk-aware Resource Brokering Strategies in Grid Computing:Broker-driven vs. User-driven Methods," TEMEP Discussion Papers 201063, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Mar 2010.
  • Handle: RePEc:snv:dp2009:201063
    as

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    File URL: ftp://147.46.237.98/DP-63.pdf
    File Function: First version, 2010
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    References listed on IDEAS

    as
    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 1-6.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

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

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms
    • L15 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Information and Product Quality
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
    • L99 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Other
    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management
    • M21 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics - - - Business Economics

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