"Control Policies for Dynamical Queues and Flow Networks"

Ketan Savla, Research Scientist, Laboratory for Information and Decision Systems, MIT

April 25th (Monday), 1:30pm
Engineering Science Building (ESB), Rm 2001

NOTE: Time change above

Queueing systems, along with flow network approximations, provide a fruitful framework for several applications such as transportation, production and data networks. In this talk, we present a novel generalization of this framework that explicitly incorporates dynamical aspects inspired by well-known empirical findings. In particular, two scenarios will be discussed. First, we present a novel dynamical queue model in which the service times depend on the utilization history of the server. For such a queue, we show that a simple threshold policy, that releases a task to the server only if its state is below a certain fixed value, is throughput-optimal. Second, we consider a dynamical flow network where the flow dynamics is driven by the difference between the inflow and outflow on the links. For such a flow network, we show that the node-wise routing policies that respond cooperatively to variations in flow densities on local links provide maximum global robustness guarantees under local information constraint. These results rely on technical tools and concepts at the intersection of dynamical systems, optimization and cognitive psychology, and provide key insights into the fundamental performance limits in presence of dynamical effects.

About Ketan Savla:

Ketan Savla is a research scientist at the Laboratory for Information and Decision Systems at MIT. He obtained his Ph.D. in Electrical Engineering and M.A. in Applied Mathematics, both in 2007, from UCSB, as well as M.S. in Mechanical Engineering from UIUC in 2004. His current research interest is in control and optimization techniques for cyber-physical systems with applications in mobile robotic networks, humans-in-loop systems, intelligent transportation systems and computational neuroscience. His awards include CDC-ECC 2005 Best Student Paper finalist and CCDC Best Thesis Award from UCSB.

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