Tools
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Moradpoor, Naghmeh, Parr, Gerard 
ORCID: https://orcid.org/0000-0002-9365-9132, McClean, Sally, Scotney, Bryan and Owusu, Gilbert
(2009)
Real-Time Data Analytics in Support of Network Resource Management Protocols.
In:
The 10th Annual Conference on teh Convergence of Telecommunications, Networking Broadcasting.
PGNet.
Abstract
Communications Networks Resource Management (RM) functions such as dynamic and static resource usage monitoring, real time resource reservation as well as advance resource reservation have been widely studied in the past few years. Research has been investigating variant techniques for addressing RM problems in different fixed and wireless networks. One of the key observations is that with the increased complexity of these networks, the function of providing real-time OAMP has resulted in an ever increased network element MIB attribute polling resulting in a data mountain. The key issue is to extract intelligence from this data using data analytical tools in an efficient manner if it is to have any impact on optimising real-time network resource utilisation as a function of demand. This paper reviews some of the recent techniques conducted in RM for different domains. We discuss data management (DM) in telecommunications and try to depict its role to support our real-time data analytics within the OAMP functions. The main purpose of this paper is to investigate RM components in groups of networks (ATM, MPLS, Wireless, EPONs) and indicate recent solutions for each group.
| Item Type: | Book Section |
|---|---|
| Uncontrolled Keywords: | resource management,data mining,optimisation,next generation networks |
| 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: | 16 Nov 2016 13:00 |
| Last Modified: | 10 Dec 2024 01:11 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/61387 |
| DOI: |
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