NDLI: Neural call admission control through virtual links estimates

Content Provider	IEEE Xplore Digital Library
Author	Herrero, I. Diaz-Estrella, A. Sandoval, F.
Copyright Year	1997
Description	Author affiliation: Dept. de Tecnologia Electron., Malaga Univ., Spain (Herrero, I.)
Abstract	A neural call admission control (NCAC) decides whether a new connection can be accepted, according to a neural network decision. Such a decision is based on traffic information (e.g. cell loss rate (CLR)) measured in real time, at the ATM node, which ensures continual training of an artificial neural network (ANN) during NCAC performance, allowing the CAC to adapt to possible changes in traffic conditions. This paper addresses the problem of providing accurate CLR information to perform optimal ANN training, which results in efficient NCAC performance. This paper proposes a novel estimation method based on measuring the CLR at virtual links with different and slower output rates. This information can be related to the real CLR, by means of an ANN, thus solving the accuracy and estimation-duration problems of real-link estimates. Prior information which allows the ANN to interpolate the real CLR is also required to establish the relationship between the virtual and the real CLRs. This information has been named zero loss bandwidth patterns.
Starting Page	133
Ending Page	140
File Size	688815
Page Count	8
File Format	PDF
ISBN	0780341961
DOI	10.1109/ATM.1997.624666
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-05-25
Publisher Place	Portugal
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Call admission control Artificial neural networks Traffic control Communication system traffic control Telecommunication traffic Quality of service Loss measurement Bandwidth Aggregates Computer networks
Content Type	Text
Resource Type	Article

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