ASIM: An Agent-based Integrated Model for Complex Networks
Complex networks comprised of large-scale distributed heterogeneous systems are everywhere, for example, in brains, networks of social and economic interactions, and the Internet. Often these systems have surprising common properties, such as power-law degree distributions, which occur in networks as different as metabolic networks and co-authorship patterns in scientific publications. Many mathematical and computational models have been developed over the past decade that attempt to explain emergent properties at a macro-level. However, understanding more detailed structure and behavior of complex networks has required the incorporation of more domain-specific features, for example, incorporating spatial distributions and business relationships into models of the Internet. This project focuses on the Internet, and Internet-like systems.
The Internet is one of the largest and most complex human artifacts ever created, and operates on many different scales, from the slow expansion of new autonomous systems, to the speed-of-light propagation of data. The Internet involves a multitude of heterogeneous systems and organizations around the world, including many within the purview of the Department of Energy (DOE). Although the DOE directly administers networks such as ESNet, much of the information flow in “open science” depends on the Internet and is outside the direct control of the DOE. The Internet also resembles many systems within the purview of the DOE, and can serve as a model for a wide variety of technological networks. There are many excellent data sets available for the Internet, allowing careful model validation.
We have developed an agent-based model of Internet-like systems, known as ASIM, and shown that by considering traffic, geography and economics, we can model existing networks more accurately than alternative models. We are using ASIM to study the effects of potential regulatory policies, more realistic traffic models, geographic country-level boundaries, and preplanned systems (such as ESNet). Our goal is to enhance understanding of complex networks, particularly with respect to predicting emergent properties and understanding the impact of different policies. We are also studying the impact of malicious behavior on complex networks, particularly the dynamic interplay between security countermeasures and attacks.
The code for the ASIM model is available for download. In addition, the data sets we used for testing the model can also be downloaded, as well as the analysis scripts we use to evaluate and compare various models.