Accession Number:

AD1052780

Title:

Traffic Congestion Analysis for a Software-Defined Network

Descriptive Note:

Technical Report

Corporate Author:

NAVAL POSTGRADUATE SCHOOL MONTEREY CA MONTEREY United States

Personal Author(s):

Report Date:

2018-03-01

Pagination or Media Count:

73.0

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.

Subject Categories:

  • Computer Systems
  • Military Operations, Strategy and Tactics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE