Enhancing Computational Efficiency in NetLogo: Best Practices for Running Large-Scale Agent-Based Models on AWS and Cloud Infrastructures

TL;DR

This paper offers best practices for optimizing NetLogo to efficiently run large-scale agent-based models on AWS and cloud platforms, reducing costs and enhancing performance.

Contribution

It provides a comprehensive guide with practical optimization strategies for NetLogo on cloud infrastructures, including cost reduction and performance improvements.

Findings

Achieved a 32% reduction in computational costs.

Demonstrated performance gains through optimized AWS instance selection.

Validated improvements using the wolf-sheep predation model.

Abstract

The rising complexity and scale of agent-based models (ABMs) necessitate efficient computational strategies to manage the increasing demand for processing power and memory. This manuscript provides a comprehensive guide to optimizing NetLogo, a widely used platform for ABMs, for running large-scale models on Amazon Web Services (AWS) and other cloud infrastructures. It covers best practices in memory management, Java options, BehaviorSpace execution, and AWS instance selection. By implementing these optimizations and selecting appropriate AWS instances, we achieved a 32\% reduction in computational costs and improved performance consistency. Through a comparative analysis of NetLogo simulations on different AWS instances using the wolf-sheep predation model, we demonstrate the performance gains achievable through these optimizations.