Identifying congestion in software-defined networks using spectral graph theory
Thomas Parker, Jamie L. Johnson, Murali Tummala, John McEachen, James W. Scrofani · 2014 48th Asilomar Conference on Signals, Systems and Computers · 2014
Software-defined networks (SDN) are an emerging technology that offers to simplify networking devices by centralizing the network layer functions and allowing adaptively programmable traffic flows. We propose using spectral graph theory methods to identify and locate congestion in a network. The analysis of the balanced traffic case yields an efficient solution for congestion identification. The unbalanced case demonstrates a distinct drop in connectivity that can be used to determine the onset of congestion. The eigenvectors of the Laplacian matrix are used to locate the congestion and achieve effective graph partitioning.