Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.646023
Title: On the application of Bayesian networks for autonomic network management
Author: Bashar, Abul
Awarding Body: Ulster University
Current Institution: Ulster University
Date of Award: 2011
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Abstract:
The quest for achieving an efficient, reliable and cost-effective network infrastructure in support of innovative and rich communication services has resulted in the advent and popularity of IP based converged Next Generation Networks (NGN). According to the ITU-T, the NGN has significant advantages such as support for end to end Quality of Service (QoS), generalised mobility, converged services between fixed & mobile networks and interworking with legacy networks. These networks require Network Management Systems (NMS), which play a key role in monitoring and administering them, to ensure smooth running of services and optimum utilisation of network resources. However, it has been observed that a disparity exists in the pace of development of the NGN and the existing NMSs. This thesis takes up the challenge of addressing this disparity by providing autonomic, intelligent and scalable NM solutions with focus on QoS Management, Distributed Monitoring & Control and Traffic Engineering. Traditional methods of network management do have shortcomings in providing the level of dependability and reliability which is required in the current NGN environment. Machine Learning (ML) approaches have gained popularity as the foundation of intelligent and automated systems, since they are capable of modelling the system behaviour through the process of learning, based on the observation of the system over a period of time. Once appropriately trained they can automatically estimate and predict future system behaviour with high accuracy and speed. Hence, the overall objective of this research is design, development and evaluation of ML based network management mechanisms for Admission Control , Distributed Monitoring & Control and Intelligent Traffic Engineering. In the pursuit of fulfilling the stated objective, the Bayesian Networks (BN) approach emerged as a viable and efficient solution to the identified specific problems.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.646023  DOI: Not available
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