Using unsupervised machine learning for fault identification in virtual machines
Abstract
Self-healing systems promise operating cost reductions in large-scale computing
environments through the automated detection of, and recovery from, faults.
However, at present there appears to be little known empirical evidence comparing the
different approaches, or demonstrations that such implementations reduce costs.
This thesis compares previous and current self-healing approaches before demonstrating
a new, unsupervised approach that combines artificial neural networks with
performance tests to perform fault identification in an automated fashion, i.e. the
correct and accurate determination of which computer features are associated with
a given performance test failure.
Several key contributions are made in the course of this research including an
analysis of the different types of self-healing approaches based on their contextual
use, a baseline for future comparisons between self-healing frameworks that
use artificial neural networks, and a successful, automated fault identification in
cloud infrastructure, and more specifically virtual machines. This approach uses
three established machine learning techniques: Naïve Bayes, Baum-Welch, and
Contrastive Divergence Learning. The latter demonstrates minimisation of human-interaction
beyond previous implementations by producing a list in decreasing
order of likelihood of potential root causes (i.e. fault hypotheses) which brings
the state of the art one step closer toward fully self-healing systems.
This thesis also examines the impact of that different types of faults have on their
respective identification. This helps to understand the validity of the data being
presented, and how the field is progressing, whilst examining the differences in
impact to identification between emulated thread crashes and errant user changes –
a contribution believed to be unique to this research.
Lastly, future research avenues and conclusions in automated fault identification
are described along with lessons learned throughout this endeavor. This includes
the progression of artificial neural networks, how learning algorithms are being
developed and understood, and possibilities for automatically generating feature
locality data.
Type
Thesis, PhD Doctor of Philosophy
Rights
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
Collections
Except where otherwise noted within the work, this item's licence for re-use is described as Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Items in the St Andrews Research Repository are protected by copyright, with all rights reserved, unless otherwise indicated.