# Lessons in Discrete Event Simulation

Part of my research involves the creation of large scale distributed systems, and while we do build these systems and deploy them, we do find that simulating them for development and research gives us an advantage in trying new things out. To that end, I employ discrete event simulation (DES) using Python’s SimPy library to build very large simulations of distributed systems, such as the one I’ve built to inspect consistency patterns in variable latency, heterogenous, partition prone networks: CloudScope.

Very briefly (perhaps I’ll do a SimPy tutorial at another date), SimPy creates an environment that dispatches and listens for events. You create these event processes in the environment by implementing generators that yield control of the execution as they’re working. As a result, the SimPy environment can call the next() method of your generator to do processing on schedule. Consider the following code:

import simpy

def wake_and_sleep(env):
state = 'Awake'

while True:
state = 'Awake' if state == 'Asleep' else 'Asleep'
print "{} at {}".format(state, env.now)

# wait 5 timesteps
yield env.timeout(5)

if __name__ == '__main__':
env = simpy.Environment()
env.process(wake_and_sleep(env))
env.run(until=100)


This simple generator function runs forever and constantly switches its state from Awake to Asleep to Awake again every 5 timesteps. If you run this you’ll get something that looks as follows:

Asleep at 0
Awake at 5
Asleep at 10
Awake at 15
Asleep at 20
...


The neat thing is that SimPy doesn’t have a counter that simply increments the timestep and checks if any events have gone off – and actually that’s the whole point of discrete event simulation: to simulate events and their interactions as they occur in order without the burden of waiting for a real time simulation. Instead, SimPy has a master schedule that is created by calling the next() method of all its processes. When you yield a timeout of 5, your next() method will be called at env.now + 5. SimPy just increments now to the next timestep that has an event in it (or the minimum time of the current schedule).

This means that it’s a bad idea to constantly yield 1 timestep timeouts. Especially if you’re doing something like checking a state for work. Instead you should register a callback to the state change that you’re looking for, and call the closure there. However, when you’re modeling real world systems that do check their state every timestep, it’s very difficult. This led me to the question:

How bad is it to constantly yield 1 timestep timeouts in large simulations?

So of course, we need data.

I created a SimPy process that would yield a timeout with a specific number of steps as a timeout, then track how many executions the process and the amount of real time that passes by. The experiment variables were the maximum number of timesteps allowed in the simulation and the number of steps to yield in the process. I selected a range of 10 maximum times between 1,000,000 and 50,000,000, with a stride of 5,000,000 and a range of steps between 1 and 10. This led to 100 runs of the simulation with each dimension pair. The results were as follows:

As you can see, there is an exponential decrease in the amount of real time taken by the system, and the amount of time you yield in your event process. Even just going from doing a check every single timestep to every other timestep will save you a lot of real time in your simulation process!

And a different view, the interaction plot is as follows:

The interact plot shows every experiment, which is a grid of max simulation time (until) and the number of steps between event process yield. The heatmap shows that the amount of real time is exponentially dependent on steps (the curve around the X access) and linearly dependent on until (there is a straight line through the center of the curves).

The code to reproduce the data and the experiment is as follows:

The code does contain some cloudscope utilities, but they’re not large and can be found in the cloudscope repository.

For the results reported, the code was run on a MacBook Pro (Retina, 15-inch, Early 2013) with a 2.8 GHz Intel Core i7 processor and 16 GB of 1600 MHz DDR3 memory, running OS X El Capitan Version 10.11.4.

The visualizations were generated with the following code:

import numpy as np
import pandas as pd
import seaborn as sns

# Set the context and style
sns.set_context('talk')
sns.set_style('whitegrid')