Eric Friedlander - Mean-Field Methods In Large Stochastic Networks

Seminar | February 14 | 3:30-5 p.m. | 540 Cory Hall

 Eric Friedlander, University of North Carolina - Chapel Hill

 Industrial Engineering & Operations Research

Abstract: Analysis of large-scale communication networks (e.g. ad hoc wireless networks, cloud computing systems, server networks etc.) is of great practical interest. The massive size of such networks frequently makes direct analysis intractable. Asymptotic approximations using hydrodynamic and diffusion scaling limits provide useful methods for approaching such problems. In this talk, we study two examples of such an analysis. In the first, the technique is applied to a model for load balancing in a large, cloud-based, storage system. In the second, we present an asymptotic method of solving control problems in such networks.

Bio: Eric Friedlander's research is focused on the modeling and analysis of large scale systems arising from communication networks and biological systems. As more and more business is conducted online, massive cloud-based storage systems and market places have created the need for models and methodology applicable on a scale that is currently impracticable. In his research, he studies these types of systems and develop tractable methods of approaching the problems which arise. In addition, he am interested in the study of data assimilation methods for complex high-dimensional systems. Data assimilation is the science of incorporating data into complex deterministic dynamical models (normally described through a system of ODE/PDE). Such a process is often complicated by the massive size of such systems and various methods have been developed to cope with this challenge.