DOI: https://doi.org/10.15368/theses.2009.184
Available at: https://digitalcommons.calpoly.edu/theses/215
Date of Award
12-2009
Degree Name
MS in Computer Science
Department/Program
Computer Science
Advisor
Franz Kurfess
Abstract
The size and speed of computer networks continue to expand at a rapid pace, as do the corresponding errors, failures, and faults inherent within such extensive networks. This thesis introduces a novel approach to interface Border Gateway Protocol (BGP) computer networks with neural networks to learn the precursor connectivity patterns that emerge prior to a node failure. Details of the design and construction of a framework that utilizes neural networks to learn and monitor BGP connection states as a means of detecting and predicting BGP peer node failure are presented. Moreover, this framework is used to monitor a BGP network and a suite of tests are conducted to establish that this neural network approach as a viable strategy for predicting BGP peer node failure. For all performed experiments both of the proposed neural network architectures succeed in memorizing and utilizing the network connectivity patterns. Lastly, a discussion of this framework's generic design is presented to acknowledge how other types of networks and alternate machine learning techniques can be accommodated with relative ease.
Included in
Artificial Intelligence and Robotics Commons, Computer and Systems Architecture Commons, OS and Networks Commons