Stochastic networks are used as models for complex systems involving
dynamic interactions subject to uncertainty. Application domains include
manufacturing, the service industry, telecommunications, and computer
systems. Networks arising in modern applications are often highly complex
and heterogeneous, with network features that transcend those of
conventional queueing models. The control and analysis of such networks
present challenging mathematical problems. In this talk, a concrete application
will be used to illustrate a general approach to the study of stochastic networks
using more tractable approximate models. Specifically, we consider a
data network model that represents the
randomly varying number of flows present in a network where bandwidth is
shared fairly amongst elastic documents. This model, introduced by Massoulie
and Roberts, can be viewed as a stochastic network with simultaneous
resource possession. Elegant fluid and diffusion approximations will be used
to study the stability and performance of this model.
The talk will conclude with
a summary of the current status and description of open problems associated
with the further development of approximate models for general stochastic
networks. This talk is based in part on joint work with W. Kang,
F. P. Kelly, and N. H. Lee.