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• W2000422216 abstract "We derive two scalability models for high-performance distributed environments using low-cost, low-performance components. On the first model, we approximate the tail of the probability distribution by using the Pareto probability distribution and then build a stochastic model to determine the maximum user-load per server according to certain quality of service parameters. On the second model, we use a probabilistic measure to determine the ratio of computing servers to storage servers and thus complete the performance model. The data of execution traces obtained from the testbed is analyzed. Hurst parameter is estimated using the Abry–Veitch estimator and distribution parameters are estimated for different probability distributions. Models based on the Pareto and Gamma probability distributions are developed and used with the data that was analyzed. There seems to be a good agreement between our models, the experimental execution traces, and simulations of HP computing environments. Specifically, the Pareto Fractal Flow model is compared with other models based on the Gamma and Gaussian distributions, such as the FGN and M-G-∞, and it seems to make better predictions under our experimental conditions. Results are presented comparing analytical models with simulation results. Copyright © 2009 John Wiley & Sons, Ltd." @default.
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• W2000422216 date "2010-08-01" @default.
• W2000422216 modified "2023-10-02" @default.
• W2000422216 title "From commodity computers to high-performance environments: scalability analysis using self-similarity, large deviations and heavy-tails" @default.
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