How hard are steady-state queueing simulations?. Ni, E. C. & Henderson, S. G. ACM Transactions on Modeling and Computer Simulation, 25(4):Article 27, 2015. ![How hard are steady-state queueing simulations? [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex Some queueing systems require tremendously long simulation runlengths to obtain accurate estimators of certain steady-state performance measures when the servers are heavily utilized. However, this is not uniformly the case. We analyze a number of single-station Markovian queueing models, demonstrating that several steady-state performance measures can be accurately estimated with modest runlengths. Our analysis reinforces the meta result that if the queue is ``well dimensioned,'' then simulation runlengths will be modest. Queueing systems can be well dimensioned because customers abandon if they are forced to wait in line too long, or because the queue is operated in the ``quality and efficiency driven regime'' where servers are heavily utilized but wait times are short. The results are based on computing or bounding the asymptotic variance and bias for several standard single-station queueing models and performance measures.
@article{nihen14,
abstract = {Some queueing systems require tremendously long simulation runlengths to obtain accurate estimators of certain steady-state performance measures when the servers are heavily utilized. However, this is not uniformly the case. We analyze a number of single-station Markovian queueing models, demonstrating that several steady-state performance measures can be accurately estimated with modest runlengths. Our analysis reinforces the meta result that if the queue is ``well dimensioned,'' then simulation runlengths will be modest. Queueing systems can be well dimensioned because customers abandon if they are forced to wait in line too long, or because the queue is operated in the ``quality and efficiency driven regime'' where servers are heavily utilized but wait times are short. The results are based on computing or bounding the asymptotic variance and bias for several standard single-station queueing models and performance measures.},
annote = {pubs/howhard.pdf},
author = {Eric Cao Ni and Shane G. Henderson},
date-added = {2016-01-10 16:07:54 +0000},
date-modified = {2018-07-16 13:39:10 +0000},
journal = {ACM Transactions on Modeling and Computer Simulation},
number = {4},
pages = {Article 27},
title = {How hard are steady-state queueing simulations?},
url_paper = {https://dl.acm.org/authorize?N654062},
volume = {25},
year = {2015}}
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