Two Risk-aware Resource Brokering Strategies in Grid Computing:Broker-driven vs. User-driven Methods
AbstractGrid 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 InfoPaper provided by Seoul National University; Technology Management, Economics, and Policy Program (TEMEP) in its series TEMEP Discussion Papers with number 201063.
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
Grid Computing; Risk Management; Self-Insurance; Risk-Performance Preference Specification;
Find related papers by 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, and Information
- 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 - - Business Administration - - - IT Management
- M21 - Business Administration and Business Economics; Marketing; Accounting - - Business Economics - - - Business Economics
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-08-21 (All new papers)
- NEP-CMP-2010-08-21 (Computational Economics)
- NEP-IAS-2010-08-21 (Insurance Economics)
- NEP-RMG-2010-08-21 (Risk Management)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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"Agent-Based Computational Economics: A Constructive Approach to Economic Theory,"
Staff General Research Papers
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- Leigh Tesfatsion, 2006. "Agent-Based Computational Economics: A Constructive Approach to Economic Theory," Computing in Economics and Finance 2006 527, Society for Computational Economics.
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