Knowledge discovery using bayesian network framework for intelligent telecommunication network management

Bashar, Abul, Parr, Gerard ORCID: https://orcid.org/0000-0002-9365-9132, McClean, Sally, Scotney, Bryan and Nauck, Detlef (2010) Knowledge discovery using bayesian network framework for intelligent telecommunication network management. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6291 LNAI. pp. 518-529.

Full text not available from this repository. (Request a copy)

Abstract

The ever-evolving nature of telecommunication networks has put enormous pressure on contemporary Network Management Systems (NMSs) to come up with improved functionalities for efficient monitoring, control and management. In such a context, the rapid deployments of Next Generation Networks (NGN) and their management requires intelligent, autonomic and resilient mechanisms to guarantee Quality of Service (QoS) to the end users and at the same time to maximize revenue for the service/network providers. We present a framework for evaluating a Bayesian Networks (BN) based Decision Support System (DSS) for assisting and improving the performance of a Simple Network Management Protocol (SNMP) based NMS. More specifically, we describe our methodology through a case study which implements the function of Call Admission Control (CAC) in a multi-class video conferencing service scenario. Simulation results are presented for a proof of concept, followed by a critical analysis of our proposed approach and its application.

Item Type: Article
Faculty \ School: Faculty of Science > School of Computing Sciences
UEA Research Groups: Faculty of Science > Research Groups > Smart Emerging Technologies
Faculty of Science > Research Groups > Cyber Security Privacy and Trust Laboratory
Faculty of Science > Research Groups > Data Science and AI
Depositing User: Pure Connector
Date Deposited: 24 Sep 2016 00:34
Last Modified: 10 Dec 2024 01:28
URI: https://ueaeprints.uea.ac.uk/id/eprint/60106
DOI: 10.1007/978-3-642-15280-1_47

Actions (login required)

View Item View Item