#distributed-systems #experiment #simulated-annealing #annealing #sim #simulation


A discrete-event simulation library aimed at high-level use-cases to quickly simulate real-world problems and run simulated experiments. Some example use cases might include simulating logistics or operations research problems, running experiments to determine optimal parameters, simulating queueing systems, distributed systems, performance engineering, and so on

18 unstable releases (3 breaking)

0.4.1 Mar 31, 2024
0.4.0 Mar 31, 2024
0.3.3 Mar 26, 2024
0.2.3 Jul 7, 2023
0.1.7 Jul 5, 2023

#131 in Math

MIT license

559 lines

simul is a discrete-event simulation library aimed at high-level use-cases to quickly simulate real-world problems and run simulated experiments.

simul is a discrete-event simulator using incremental time progression, with M/M/c queues for interactions between agents. It also supports some forms of experimentation and simulated annealing to replicate a simulation many times, varying the simulation parameters.




Experimental and unstable. Almost all APIs are expected to change.

Basic usage

simul = "0.3.1"
use simul::Simulation;
use simul::agent::*;

// Runs a simulation with a producer that produces work at every tick of
// discrete time (period=1), and a consumer that cannot keep up (can only
// process that work every third tick).
let mut simulation = Simulation::new(SimulationParameters {
    // We pass in two agents:
    //   one that produces -> consumer every tick
    //   one that simply consumes w/ no side effects every third tick
    agents: vec![
        periodic_producing_agent("producer".to_string(), 1, "consumer".to_string()),
        periodic_consuming_agent("consumer".to_string(), 3),
    // You can set the starting epoch for the simulation. 0 is normal.
    starting_time: 0,
    // Whether to collect telemetry on queue depths at every tick.
    // Useful if you're interested in backlogs, bottlenecks, etc. Costs performance.
    enable_queue_depth_metric: true,
    /// Records a metric on the number of cycles agents were asleep for.
    enable_agent_asleep_cycles_metric: true,
    // We pass in a halt condition so the simulation knows when it is finished.
    // In this case, it is "when the simulation is 10 ticks old, we're done."
    halt_check: |s: &Simulation| s.time == 10,


Poisson-distributed example w/ Plotting

Here's an example of an outputted graph from a simulation run. In this simulation, we show the average waiting time of customers in a line at a cafe. The customers arrive at a Poisson-distributed arrival rate (lambda<-60.0) and a Poisson-distributed coffee-serving rate with the same distribution.

This simulation maps to the real world by assuming one tick of discrete-simulation time is equal to one second.

Basically, the barista serves coffees at around 60 seconds per drink and the customers arrive at about the same rate, both modeled by a stochastic Poisson generator.

This simulation has a halt_check condition of the simulation's time being equal to 60*60*12, representing a full 12-hour day of the cafe being open.

This is a code example for generating the above.

use plotters::prelude::*;
use rand_distr::Poisson;
use simul::agent::*;
use simul::*;
use std::path::PathBuf;

fn main() {

fn run_example_cafe_simulation() -> Result<(), Box<dyn std::error::Error>> {
    let mut simulation = Simulation::new(SimulationParameters {
        agents: vec![
            poisson_distributed_consuming_agent("Barista".to_string(), Poisson::new(60.0).unwrap()),
        starting_time: 0,
        enable_queue_depth_metric: true,
        halt_check: |s: &Simulation| s.time == 60 * 60 * 12,




Issues, bugs, features are tracked in TODO.org


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