SIMoN

System Integration with Multiscale Networks

Copyright 2020 The Johns Hopkins University Applied Physics Laboratory

Licensed under the MIT License

[Copyrights](Third Party Copyrights.pdf) for Third Party Software

Contact: simon@jhuapl.edu

Description

With the rise of globalization, climate change, population growth, and resource constraints, new modeling techniques are needed to assess the sustainability of our future resources. These methods must adapt to highly coupled domains, accommodate new models and data as they emerge, facilitate validation through model comparison, and handle the patchwork of data and models available with different units, definitions, and geo-temporal scales.

To address this challenge, the SIMoN modeling framework integrates independently-designed predictive models into a cohesive system, in order to produce a unified model. While many useful models are limited to predicting only a single isolated component of a larger system, SIMoN is able to connect models together so that collectively they can provide a more complete representation of the global system and its dynamics.

Requirements

Supported operating systems:

• Linux
• macOS

Software:

• Python >= 3.6
• Docker >= 18.09.6
• Docker Compose >= 1.23.2

Setup

SIMoN uses Docker and Compose to run its models in separate containers. To run SIMoN, clone the repo and install these tools.

The Docker containers used for the models are built from the Ubuntu 18.04 image, with the Python 3.6 package layered on top.

The Docker container used for the database is a MongoDB image.

Additionally, install make, so that the shell commands that operate SIMoN can be executed more easily using the Makefile.

The Docker commands in the provided scripts assume that you are a privileged user. To use Docker as a non-root user, add your user to the docker group:

sudo usermod -aG docker <your_username>

Log out and back in for this to take effect.

Usage

1. To start SIMoN:
   • make all
   • Use docker logs broker -f to track output from the broker container. The increment step "incstep" should increase over time as models publish their data, and the mongodb container should populate with documents (database: broker; collection: sub).
   • Use docker logs simon_your_model_name_1 -f to track output from the model named your_model_name.
2. To shut down SIMoN:
   • make stop to stop all the SIMoN containers.
   • make clean to stop and remove all the SIMoN containers.
   • make purge to stop and remove all the SIMoN containers, and also remove their images.

Visualization

SIMoN stores all of the data outputs from the models as documents in a Mongo database (the simon_mongodb container).

You can retrieve a document and save it as a JSON file using the export.sh bash script in the viz directory.

Once you've retrieved a document and saved it as a JSON file, plot the data on a choropleth map using the plot.py script in the viz directory. (Just make sure to pip install requirements.txt first.)

cd viz/
./export.sh <model_name> <year>
pip install -r requirements.txt
python plot.py <your_mongo_doc>.json

A new HTML file will be created. Open this file in a web browser to display the Bokeh visualization.

Architecture

SIMoN is written in Python, and uses Docker to manage its models and their integration. In order to increase flexibility and scalability, each model runs in discrete iterations (called increment steps) within its own Docker container. An additional container hosts the system's centralized Broker, which orchestrates model runs by receiving each model's data outputs via a ZeroMQ publish-subscribe messaging pattern, then redirecting the data to any models that request it. The models can then use this data as their inputs for the next incremental step in the system’s synchronized run.