Agent-based models (ABMs) can be computationally intensive, especially when running Monte Carlo simulations that require thousands of model runs. Cloud computing services like Amazon EC2 provide a cost-effective way to access the computing power needed for these simulations.

Why Use Cloud Computing for ABMs?

Running an ABM often requires:

  • Hundreds or thousands of model runs for sensitivity analysis
  • Large amounts of memory for complex agent populations
  • Long runtimes that would tie up local machines

Cloud computing allows you to:

  • Scale up to many instances for parallel runs
  • Pay only for the compute time you use
  • Access powerful hardware without capital investment

Setting Up an EC2 Instance

1. Choose an Instance Type

For CPU-intensive ABM work, compute-optimized instances work well:

  • c5.xlarge: Good balance of cost and performance
  • c5.4xlarge: For larger models or faster parallel processing

2. Configure Your Instance

Create an Amazon Machine Image (AMI) with your ABM environment:

# Update system
sudo apt-get update && sudo apt-get upgrade

# Install Python and dependencies
sudo apt-get install python3 python3-pip
pip3 install numpy scipy pyabm

# Install R if needed
sudo apt-get install r-base

3. Upload Your Model

Use scp to transfer your model files:

scp -i your-key.pem model_files.zip ec2-user@your-instance:~/

Running Parallel Simulations

To take full advantage of EC2, run multiple simulations in parallel:

from multiprocessing import Pool
from pyabm import Model

def run_simulation(params):
    model = Model(params)
    return model.run()

# Run 100 simulations across all available cores
with Pool() as p:
    results = p.map(run_simulation, parameter_sets)

Cost Optimization Tips

  • Use spot instances for up to 90% cost savings
  • Stop instances when not in use
  • Use S3 for storing results instead of keeping instances running
  • Consider AWS Batch for large-scale batch processing

Conclusion

Cloud computing opens up new possibilities for ABM research by providing access to scalable computing resources on demand. For large-scale simulations, the cost and convenience benefits make it an attractive option.