Use SimPy to simulate Chord distributed system

Question

I am doing some research on several distributed systems such as Chord, and I would like to be able to write algorithms and run simulations of the distributed system with just my desktop.

In the simulation, I need to be able to have each node execute independently and communicate with each other, while manually inducing elements such as lag, packet loss, random crashes etc. And then collect data to estimate the performance of the system.

After some searching, I find SimPy to be a good candidate for my purpose.

Would SimPy be a suitable library for this task? If yes, what are some suggestions/caveats for implementing such a system?

Answer 1

I would say yes.

I used SimPy (version 2) for simulating arbitary communication networks as part of my doctorate. You can see the code here:

https://github.com/IncidentNormal/CommNetSim

It is, however, a bit dense and not very well documented. Also it should really be translated to SimPy version 3, as 2 is no longer supported (and 3 fixes a bunch of limitations I found with 2).

Some concepts/ideas I found to be useful:

• Work out what you want out of the simulation before you start implementing it; communication network simulations are incredibly sensitive to small design changes, as you are effectively trying to monitor/measure emergent behaviours from the system.
• It's easy to start over-engineering the simulation, using native SimPy objects is almost always sufficient when you strip away the noise from your design.
• Use Stores to simulate mediums for transferring packets/payloads. There is an example like this for simulating latency in the SimPy docs: https://simpy.readthedocs.io/en/latest/examples/latency.html
• Events are tricky - as they can only fire once per simulation step, so often this can be the source of bugs as behaviour is effectively lost if multiple things fire the same event in a step. For robustness, try not to use them to represent behaviour in communication networks (you rarely need something that low-level), as mentioned above - use Stores instead as these act like queues by design.
• Pay close attention to the probability distributions you use to generating randomness. Expovariate distributions are usually closer to simulating natural systems than uniform distributions, but make sure to check every distribution you use for sanity. Generating network traffic usually follows a Poisson distribution, for example, and data volume often follows a Power Law (Pareto) distribution.