Identifying Congestion in Software-Defined Networks Using Spectral Graph Theory
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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.
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Malveo, April E. (Monterey, California. Naval Postgraduate School, 2013-03);This thesis introduces an integer linear program called the Minimum Cost Flow with Congestion Assignment (MCF-CA) model. MCF-CA is a multi-period evacuation model that uses a novel approach called congestion assignment to ...
Johnson, Jamie L. (Monterey, California: Naval Postgraduate School, 2014-09);In this thesis, we propose a new software defined network monitoring scheme that provides the controller with a method to determine network states for the purpose of updating flow rules for network control and management. ...
Maxie, Moniqua J. (Monterey, California: Naval Postgraduate School, 2018-03);The objective of this thesis is to implement an anomaly-detection method that can be used to detect congestion in a software-defined network. The method incorporates spectral graph theory and phantom node techniques. The ...