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Markov Chain Analysis for Large-Scale Grid Systems
Markov Chain Analysis for Large-Scale Grid Systems
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In large-scale grid systems with decentralized control, the interactions of many service providers and consumers will likely lead to emergent global system behaviors that result in unpredictable, often detrimental, outcomes. This possibility argues for developing analytical tools to allow understanding, and prediction, of complex system behavior in order to ensure availability and reliability of grid computing services. This paper presents an approach for using piece-wise homogeneous Discrete T…

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In large-scale grid systems with decentralized control, the interactions of many service providers and consumers will likely lead to emergent global system behaviors that result in unpredictable, often detrimental, outcomes. This possibility argues for developing analytical tools to allow understanding, and prediction, of complex system behavior in order to ensure availability and reliability of grid computing services. This paper presents an approach for using piece-wise homogeneous Discrete Time Markov chains to provide rapid, potentially scalable, simulation of large-scale grid systems. This approach, previously used in other domains, is used here to model dynamics of largescale grid systems.

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In large-scale grid systems with decentralized control, the interactions of many service providers and consumers will likely lead to emergent global system behaviors that result in unpredictable, often detrimental, outcomes. This possibility argues for developing analytical tools to allow understanding, and prediction, of complex system behavior in order to ensure availability and reliability of grid computing services. This paper presents an approach for using piece-wise homogeneous Discrete Time Markov chains to provide rapid, potentially scalable, simulation of large-scale grid systems. This approach, previously used in other domains, is used here to model dynamics of largescale grid systems.

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