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Ph.D. Dissertation Proposal Defense- Chang Liu

20 Jan
Wednesday, 01/20/2016 2:20pm to 4:00pm
Computer Science Building, Room 140
Ph.D. Dissertation Proposal Defense
Speaker: Chang Liu

"Inference in Networking Systems with Designed Measurements"

Networking systems consisted of network infrastructures and the end-hosts has been essential in supporting our daily communication, delivering huge amount of content and large number of services, and providing large scale distributed computing. To monitor and optimize the performance of such networking systems, or to provide flexible functionalities for the applications running on top of the systems, it is important to know the internal metrics of the networking systems such as link loss rates or path delays. But the internal metrics are often not directly available due to the scale and complexity of the networking systems. This motivated the techniques of inference on internal metrics through available measurements. 

In this thesis, I investigate inference methods on networking systems from multiple aspects. In the context of mapping users to servers in content delivery networks, we show that letting user select a server that provides good performance from a set of servers that are randomly allocated to the user can lead to optimal server allocation, of which a key element is to infer the work load on the servers using the performance feedback. For network tomography, where the objective is to estimate link metrics (loss rate, delay, etc.) using end-to-end measurements, we show that the information of each end-to-end measurement can be quantized by fisher information and the estimation error of link metrics can be efficiently reduced if the allocation of measurements on paths is designed to maximize the overall information. Last but not least, in the context of finding the most robust path for routing from a source to a destination in a network while minimizing the cost of exploring lossy paths, the trade-off between exploiting the best paths that is estimated and taking the risk to explore worse paths for more information is investigated, and adaptive learning methods is developed. The performance of the developed techniques are evaluated with simulations.

Advisor: Donald Towsley