NDLI: A neural based call admission control in ATM networks to provide multiple QOS

Content Provider	IEEE Xplore Digital Library
Author	Khalil, I. Ali, B.M. Bidin, A.R. Mukerji, M.R. Ahmed, S.
Copyright Year	1994
Description	Author affiliation: Dept. of Electron. & Comput. Eng., Univ. Pertanian Malaysia, Selangor, Malaysia (Khalil, I.; Ali, B.M.; Bidin, A.R.; Mukerji, M.R.; Ahmed, S.)
Abstract	In high-speed asynchronous transfer mode (ATM) networks several classes of traffic streams with widely varying traffic characteristics and quality of service requirements are statistically multiplexed and share common switching and transmission resources. In such an environment we need a traffic control scheme which manages the required QOS of each class individually. We propose a neural network (NN) approach to estimate the cell loss probability (CLP) of bursty sources for call admission control (CAC) purpose in ATM environment. Training data set are obtained from a CLP method advocated by Miyao (see IEEE GLOBECOM'93, p.1398-1403, 1993), and this training is done off-line to estimate the CLP in a real time environment. We choose this method as it considers the CLP due to long-term fluctuations in the aggregate cell rate as well as short-term fluctuations in cell interarrival time and hence provides a higher accuracy of loss probabilities than traditional methods. The accuracy of the training set is crucial in the NN based training scheme to design a robust call admission controller. We find that our scheme performs consistently well in withstanding the changes in the burst duration parameters and is also suitable from the point of view of individual call quality. In order to discuss performance aspects the scheme has been compared with other cell loss estimation methods. Our simulation shows that the NN approach outperforms the conventional methods in terms of accuracy.<>
Starting Page	510
Ending Page	514
File Size	392593
Page Count	5
File Format	PDF
ISBN	0780320468
DOI	10.1109/ICCS.1994.474200
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-11-14
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Call admission control Asynchronous transfer mode Neural networks Communication system traffic control Quality of service Fluctuations Traffic control Environmental management Probability Training data
Content Type	Text
Resource Type	Article

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