A framework for the quantitative analysis of performance, security and database
management within a network system (e.g. a cloud computing platform) is presented
within this research. Our study provides a methodology for modelling and
quantitatively generating significant metrics needed in the evaluation of a network
system. To narrow this research, a study is carried-out into the quantitative modelling
and analysis of performance, security and database management trade-offs in cloud
computing platforms, based on Stochastic Activity Networks (SANs) and combined
metrics.
Cloud computing is an innovative distributed computing archetypal based on the
infrastructure of the internet providing computational power, application, storage and
infrastructure services. Security mechanisms such as: batch rekeying, intrusion
detection, encryption/decryption or security protocols come at the expense of
performance and computing resources consumption. Furthermore, database
management processing also has an adverse effect on performance especially in the
presence of big data. Stochastic Activity Networks (SANs) that offer synchronisation, timeliness and parallelism are proposed for the modelling and quantitative evaluations
of ‘optimal’ trade-offs involving performance, security and database management.
Performance modelling and analysis of computer network systems has mostly been
considered of utmost importance. Quantification of performance for a while has been
assessed using stochastic models with a rising interest in the quantification of security
stochastic modelling being applied to security problems. Quantitative techniques that
includes analytical valuations founded on queuing theory, discrete-event simulations
and correlated approximations have been utilised in the examination of performance.
Security suffers from the point that no interpretations can be made in an optimal case.
The most consequential security metrics are in analogy with reliability metrics.
The express rate at which data grows increases the prominence for research into the
design and development of cloud computing models that manages the workload
intensity and are suitable for data exploration. Handling big data especially within
cloud computing is a resource consuming, time-demanding and challenging task that
necessitates titanic computational infrastructures to endorse successful data
exploration. We present an improved Security State Transition Diagram (SSTD) by adding a new
security state (Failed/Freeze state). The presence of this new security state signifies a
security position of the computing network system were the implemented security
countermeasures cannot handle the security attacks and the system fails completely.
In a more sophisticated security system, when the security countermeasure(s) cannot
in any form categorise the security attack, the network system is moved to the
Failed/Freeze security state. At this security state, the network system can only resume
operation when restored by the system administrator.
In this study, we propose a cloud computing system model, defined security
countermeasures and evaluated the optimisation problems for the trade-offs between
performance, security and database management using SANs formalism. We
designed, modelled and implemented dependency within our presented security
system, developing interaction within the security countermeasures using our
proposed Security Group Communication System (SGCS). The choice of Petri-Nets enables the understanding and capturing of specified metrics at different stages of the
proposed cloud computing model.
In this thesis, an overview of cloud computing including its classification and services
is presented in conjunction with a review of existing works of literature. Subsequently,
a methodology is proposed for the quantitative analysis of our proposed cloud
computing model of performance-security-database trade-offs using Möbius
simulator. Additionally, numerical experiments with relevant interpretations are
presented and appropriate interpretations are made. We identified that there are system
parameters that can be used to optimise the presented abstract combined metrics but
they are optimal for neither performance or security or database management
independently. Founded on the proposed quantitative simulation model framework,
reliable numerical experiments were observed and indicated scope for further
extensions of this work. For example, the use of Machine Learning (ML) or Artificial
Intelligence (AI) in the predictive and prevention aspects of the security systems.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/19249 |
| Date | January 2020 |
| Creators | Akinyemi, Akinwale A. |
| Contributors | Kouvatsos, Demetres D. |
| Publisher | University of Bradford, Faculty of Engineering and Informatics |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Thesis, doctoral, PhD |
| Rights | <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>. |
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