🤖 AI Summary
This study addresses the significant computational overhead, performance instability, and high costs commonly encountered when running large-scale agent-based models (ABMs) in NetLogo. To tackle these challenges, the authors propose the first cloud deployment optimization framework specifically designed for large-scale NetLogo ABMs, which systematically integrates memory management, JVM parameter tuning, BehaviorSpace execution strategies, and AWS instance selection. Through experiments on the canonical wolf-sheep predation model, the framework quantitatively evaluates the impact of different cloud instances on performance and cost. The results demonstrate a 32% reduction in computational expenses while substantially improving runtime stability and efficiency.
📝 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.