# Simulating queueing systems with simmer

**FishyOperations**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

We are very pleased to announce that a new release of **simmer**, the Discrete-Event Simulator for R, is on CRAN. There are quite a few changes and fixes, with the **support of preemption** as a star new feature. Check out the complete set of release notes here.

Let’s *simmer* for a bit and see how this package can be used to simulate queueing systems in a very straightforward way.

## The M/M/1 system

In Kendall’s notation, an M/M/1 system has exponential arrivals (**M**/M/1), a single server (M/M/**1**) with exponential service time (M/**M**/1) and an inifinite queue (implicit M/M/1/**(infty)**). For instance, people arriving at an ATM at rate (lambda), waiting their turn in the street and withdrawing money at rate (mu).

Let us remember the basic parameters of this system:

whenever (rho < 1). If that is not true, it means that the system is unstable: there are more arrivals than the server is capable of handling, and the queue will grow indefinitely.

The simulation of an M/M/1 system is quite simple using `simmer`

. The trajectory-based design, combined with `magrittr`

’s pipe, is very *verbal* and self-explanatory.

library(simmer) set.seed(1234) lambda <- 2 mu <- 4 rho <- lambda/mu # = 2/4 mm1.trajectory <- create_trajectory() %>% seize("resource", amount=1) %>% timeout(function() rexp(1, mu)) %>% release("resource", amount=1) mm1.env <- simmer() %>% add_resource("resource", capacity=1, queue_size=Inf) %>% add_generator("arrival", mm1.trajectory, function() rexp(1, lambda)) %>% run(until=2000)

Our package provides convenience plotting functions to quickly visualise the usage of a resource over time, for instance. Down below, we can see how the simulation converges to the theoretical average number of customers in the system.

library(ggplot2) # Evolution of the average number of customers in the system graph <- plot_resource_usage(mm1.env, "resource", items="system") # Theoretical value mm1.N <- rho/(1-rho) graph + geom_hline(yintercept=mm1.N)

It is possible also to visualise, for instance, the instantaneous usage of individual elements by playing with the parameters `items`

and `steps`

.

plot_resource_usage(mm1.env, "resource", items=c("queue", "server"), steps=TRUE) + xlim(0, 20) + ylim(0, 4)

We may obtain the time spent by each customer in the system and we compare the average with the theoretical expression.

mm1.arrivals <- get_mon_arrivals(mm1.env) mm1.t_system <- mm1.arrivals$end_time - mm1.arrivals$start_time mm1.T <- mm1.N / lambda mm1.T ; mean(mm1.t_system) ## [1] 0.5 ## [1] 0.5012594

It seems that it matches the theoretical value pretty well. But of course we are picky, so let’s take a closer look, just to be sure (and to learn more about `simmer`

, why not). Replication can be done with standard R tools:

library(parallel) envs <- mclapply(1:1000, function(i) { simmer() %>% add_resource("resource", capacity=1, queue_size=Inf) %>% add_generator("arrival", mm1.trajectory, function() rexp(1, lambda)) %>% run(1000/lambda) %>% wrap() })

*Et voilà!* Parallelizing has the shortcoming that we lose the underlying C++ objects when each thread finishes, but the `wrap`

function does all the magic for us retrieving the monitored data. Let’s perform a simple test:

library(dplyr) t_system <- get_mon_arrivals(envs) %>% mutate(t_system = end_time - start_time) %>% group_by(replication) %>% summarise(mean = mean(t_system)) t.test(t_system$mean) ## ## One Sample t-test ## ## data: t_system$mean ## t = 344.14, df = 999, p-value < 2.2e-16 ## alternative hypothesis: true mean is not equal to 0 ## 95 percent confidence interval: ## 0.4953154 0.5009966 ## sample estimates: ## mean of x ## 0.498156

Good news: the simulator works. Finally, an M/M/1 satisfies that the distribution of the time spent in the system is, in turn, an exponential random variable with average (T).

qqplot(mm1.t_system, rexp(length(mm1.t_system), 1/mm1.T)) abline(0, 1, lty=2, col="red")

## M/M/c/k systems

An M/M/c/k system keeps exponential arrivals and service times, but has more than one server in general and a finite queue, which often is more realistic. For instance, a router may have several processor to handle packets, and the in/out queues are necessarily finite.

This is the simulation of an M/M/2/3 system (2 server, 1 position in queue). Note that the trajectory is identical to the M/M/1 case.

lambda <- 2 mu <- 4 mm23.trajectory <- create_trajectory() %>% seize("server", amount=1) %>% timeout(function() rexp(1, mu)) %>% release("server", amount=1) mm23.env <- simmer() %>% add_resource("server", capacity=2, queue_size=1) %>% add_generator("arrival", mm23.trajectory, function() rexp(1, lambda)) %>% run(until=2000)

In this case, there are rejections when the queue is full.

mm23.arrivals <- get_mon_arrivals(mm23.env) mm23.arrivals %>% summarise(rejection_rate = sum(!finished)/length(finished)) ## rejection_rate ## 1 0.02065614

Despite this, the time spent in the system still follows an exponential random variable, as in the M/M/1 case, but the average has dropped.

mm23.t_system <- mm23.arrivals$end_time - mm23.arrivals$start_time # Comparison with M/M/1 times qqplot(mm1.t_system, mm23.t_system) abline(0, 1, lty=2, col="red")

**leave a comment**for the author, please follow the link and comment on their blog:

**FishyOperations**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.