Computationally Efficient Simulation of Queues: The R Package queuecomputer

TL;DR

The paper introduces the R package queuecomputer, enabling fast simulation of complex queueing networks, significantly outperforming existing discrete-event simulation packages, and facilitating parameter inference for real-world systems.

Contribution

It presents a novel, highly efficient simulation method for general queueing networks within an R package, enhancing modeling and inference capabilities.

Findings

Speedups of over 100x compared to existing packages

Validated accuracy through replication of other packages' outputs

Successfully modeled real-world systems like call centers and airports

Abstract

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.