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Ph.D. Proposal: Timothy Curry
March 9, 2023 @ 9:00 am - 10:30 am EST
Title: Optimal Routing Configurations for Software-Defined Networks
Ph.D. Candidate: Timothy Curry
Major Advisors: Dr. Benjamin Fuller, Dr. Laurent Michel
Advisory Committee: Dr. Amir Herzberg, Dr. Minmei Wang
Date/Time: Thursday, March 9th, 2023, 9:00 am
Location: HBL 1102
Webex Link (if joining remotely): https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=mf890a8f4206581901e3a1f015786ea9f
Meeting Number: 2624 107 2741
Password: Pb3qPZ3M2sD
Abstract
Network configuration is a crucial, yet delicate, task for any enterprise. When developing a configuration, one must consider multiple aspects such as functionality, performance, and security. Such properties are often conflicting, making it difficult for human network engineers to quickly design a desirable configuration. This task is a perfect application for discrete optimization technologies. One can create a model that ensures certain crucial networking properties are upheld while the other properties are optimized to the specifications needed for any particular use case.
Software-defined networks (SDNs) provide a programmable network infrastructure where global network information can be used to inform routing decisions. Due to their flexibility and programmability, SDNs are well-suited to adopt optimization frameworks that develop routing configurations. This dissertation explores discrete optimization models designed to develop SDN routing configurations. These models automate routing decisions for network flows while preventing the presence of unwanted flows. Network security is also considered, either jointly with routing or separately in a second stage with a dedicated model. The notion of security is explored over several different metrics, including attack graphs and a novel elastic string trust model. By contemplating both functionality and security, either together or in tandem, the frameworks are able to generate routing configurations to initialize networks from scratch or quickly altering routing in response to suspicious network activity. These approaches are tested on the fat-tree topology, a commonly deployed data center network topology.
