Computationally Efficient Simulation of Queues: The R Package queuecomputer

Computationally Efficient Simulation of Queues: The R Package queuecomputer

queues, queueing theory, discrete event simulation, operations research, approximate Bayesian computation, R

Large networks of queueing systems model important real-world systems such as
MapReduce clusters, web-servers, hospitals, call centers and airport passenger terminals.
To model such systems accurately, we must infer queueing parameters from data. Unfortunately, for many queueing networks there is no clear way to proceed with parameter
inference from data. Approximate Bayesian computation could offer a straightforward
way to infer parameters for such networks if we could simulate data quickly enough.
We present a computationally efficient method for simulating from a very general set
of queueing networks with the R package queuecomputer. Remarkable speedups of more
than 2 orders of magnitude are observed relative to the popular DES packages simmer
and simpy. We replicate output from these packages to validate the package.
The package is modular and integrates well with the popular R package dplyr. Complex queueing networks with tandem, parallel and fork/join topologies can easily be built
with these two packages together. We show how to use this package with two examples:
a call center and an airport terminal.

Anthony Ebert

https://www.jstatsoft.org/article/view/v095i05

Queensland University
of Technology
ACEMS

October 2020

Fajar bagus W

PDF

Inggris

Text