An Application of Neural Networks in the Connection Admission Control of ATM Networks

Author

Vo, Viet Minh Nhat

Author_Institution

Fac. of Hospitality & Tourism, Hue Univ., Vietnam

fYear

2009

fDate

13-17 July 2009

Firstpage

1

Lastpage

5

Abstract

The connection admission control (CAC) in ATM networks is a flow controlling function that decides to allow or not a new connection joining in the network. This decision is usually based on the status of the current ATM network as its available resources, flow parameters and the quality of registered service (QoS) of new connections joining in the network as well as existing connections. This article proposes a CAC model in which neural network is used as a tool to maximize the number of admitted connections, the "profit" derived from accepted connections (thought from their QoS) or simply the used bandwidth.

Keywords

asynchronous transfer mode; neurocontrollers; quality of service; telecommunication congestion control; ATM network; CAC model; QoS; connection admission control; flow controlling function; flow parameter; neural network; quality of registered service; Admission control; Analytical models; Asynchronous transfer mode; Bandwidth; Bit rate; Communication system control; Fluid flow measurement; Hopfield neural networks; Neural networks; Quality of service;

fLanguage

English

Publisher

ieee

Conference_Titel

Computing and Communication Technologies, 2009. RIVF '09. International Conference on

Conference_Location

Da Nang

Print_ISBN

978-1-4244-4566-0

Electronic_ISBN

978-1-4244-4568-4

Type

conf

DOI

10.1109/RIVF.2009.5174618

Filename

5174618