Even simple, deterministic rules can generate interesting behavior in dynamical systems. This dissertation examines some real world systems for which fairly simple, locally defined rules yield useful or interesting properties in the system as a whole. In particular, we study routing in peer-to-peer networks and the motion of crystal steps.

Peers can vary by three orders of magnitude in their capacities to process network traffic. This heterogeneity inspires our use of "proportionate load balancing," where each peer provides resources in proportion to its individual capacity. We provide an implementation that employs small, local adjustments to bring the entire network into a global balance. Analytically and through simulations, we demonstrate the effectiveness of proportionate load balancing on two routing methods for de Bruijn graphs, introducing a new "reversed" routing method which performs better than standard forward routing in some cases.

The prevalence of peer-to-peer applications prompts companies to locate the hosts participating in these networks. We explore the use of supervised machine learning to identify peer-to-peer hosts, without using application-specific information. We introduce a model for "triples," which exploits information about nearly contemporaneous flows to give a statistical picture of a host's activities. We find that triples, together with measurements of inbound vs. outbound traffic, can capture most of the behavior of peer-to-peer hosts.

An understanding of crystal surface evolution is important for the development of modern nanoscale electronic devices. The most commonly studied surface features are steps, which form at low temperatures when the crystal is cut close to a plane of symmetry. Step bunching, when steps arrange into widely separated clusters of tightly packed steps, is one important step phenomenon. We analyze a discrete model for crystal steps, in which the motion of each step depends on the two steps on either side of it. We find an time-dependence term for the motion that does not appear in continuum models, and we determine an explicit dependence on step number.