Date of Award

2017

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

Committee Chair

Daniel Rochowiak

Committee Member

Harry Delugach

Committee Member

Letha Etzkorn

Subject(s)

Artificial intelligence., Neural networks (Computer science), Software-defined networking (Computer network technology), OpenFlow (Computer network protocol)

Abstract

Artificial Neural Networks (ANNs) were used to classify neural network flows by flow size. After training the neural network was able to predict the size of a flows with 87% accuracy with a Feed Forward Neural Network. This demonstrates that flow based routers can prioritize candidate flows with a predicted large number of packets for priority insertion into hardware content-addressable memory.

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