首页|Hierarchical Bayesian Parameter Estimation of Queueing Systems using Utilization Data
Hierarchical Bayesian Parameter Estimation of Queueing Systems using Utilization Data
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NSTL
Utilization data is a kind of time-series data that consists of the proportion of the system's busy time in a fixed time interval. Utilization data can indicate the status of servers in a computer system, such as CPU utilization. Unfortunately, estimating model parameters from utilization data is challenging due to the inability to obtain the exact job arrival time and service time. Moreover, the maximum likelihood estimation(MLE) method tends to be sensitive, which may cause an overfitting problem. In this paper, a hierarchical Bayes (HB) based approach is proposed to estimate the parameters of queueing systems from utilization data. Specifically, a time non-homogeneous queueing system M_t/M/1/K whose job arrival follows a non-homogeneous Poisson process (NHPP) is supposed. Then, a series of homogeneous Poisson processes (HPP) is approximated to simplify the NHPP. Finally, the HB method is applied to estimate parameters of the M_t/M/1/K to address the sensitive issue of the MLE method. In numerical experiments, the effectiveness of the proposed HB-based approach with CPU utilization data is validated. In addition, the statistical properties of estimated parameters with MLE and HB are also studied in experiments.
NSTL
