We consider the problem of forecasting multi-class job flow times in a resource-sharing environment. We assume the deviation of flow times in each class from the class nominal value follows an exponential distribution with its parameter following a gamma distribution. A large simulation experiment is conducted to assess and compare the performance of the Bayes and empirical Bayes forecasting methods under differing model assumptions. Simulation results show that non-parametric empirical Bayes methods are more efficient and robust relative to the parametric empirical Bayes.
Empirical Bayes forecasting methods for job flow times
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