Traffic Congestion Analysis for a Software-Defined Network

Abstract

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 experimental implementation of spectral graph theory used eigenvalue-eigenvector solutions to characterize a mathematical model of the networks topology. In this thesis, we used the phantom node technique to determine congestion in the network by using a virtual node to set the threshold for available link capacity, or the maximum amount of traffic, that can cross the links in the network before the links are considered congested. Results show that when the network is congested, a shift occurs in the eigenvalue and eigenvalue index spectrum.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2018
Accession Number
AD1052780

Entities

People

  • Moniqua J. Maxie

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Cyber
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Anomaly Detection
  • Application Software
  • Change Detection
  • Computer Network Security
  • Computer Networks
  • Computers
  • Cyberattacks
  • Denial Of Service Attack
  • Detection
  • Instruction Set Architecture
  • Mathematical Models
  • Network Protocols
  • Network Science
  • Network Topology
  • Operating Systems
  • Software Defined Networks
  • Two Dimensional

Fields of Study

  • Computer science

Readers

  • Computer Networking
  • Linear Algebra
  • Radar Systems Engineering.