Stochastic models of processing networks arise in a
wide variety of applications in science and engineering, e.g., in high-tech
manufacturing, transportation, telecommunications, computer systems,
customer service systems, and biochemical reaction networks. These
"stochastic processing networks" typically have entities, such as
jobs, vehicles, packets, customers or molecules, that move along paths or
routes, receive processing from various resources, and that are subject to
the effects of stochastic variability through such variables as arrival
times, processing times and routing protocols. Networks arising in modern
applications are often heterogeneous in that different entities share
(i.e., compete for) common network resources. Frequently the processing
capacity of resources is limited and there are bottlenecks, resulting in
congestion and delay due to entities waiting for processing. The control
and analysis of such networks present challenging mathematical problems.
This talk will explore the effects of resource sharing
in stochastic networks and describe associated mathematical analysis based
on elegant fluid and diffusion approximations. Illustrative examples will
be drawn from biology and telecommunications.
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