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.
Recommended Citation
Arnold, Michael, "Predictive networking and optimization for flow-based networks" (2017). Theses. 210.
https://louis.uah.edu/uah-theses/210