Comparison on five estimation approaches of intensity for a queueing system with short run

Traffic intensity is an important measure for assessing performance of a queueing system. In this paper, we propose a consistent and asymptotically normal estimator (CAN) of intensity for a queueing system with distribution-free interarrival and service times. Using this estimator and its estimated variance, a 100(1 ? α)% asymptotic confidence interval of the intensity is constructed. Also, four bootstrap approaches—standard bootstrap, Bayesian bootstrap, percentile bootstrap, and bias-corrected and accelerated bootstrap are also applied to develop the confidence intervals of the intensity. A comparative analysis is conducted to demonstrate performances of the five confidence intervals of the intensity for a queueing system with short run data.