Empirical Bayes forecasting methods for job flow times

Abstract

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.