DocumentCode :
1126635
Title :
Learning-based resource optimization in asynchronous transfer mode (ATM) networks
Author :
Al-Sharhan, Salah ; Karray, Fakhri ; Gueaieb, Wail
Author_Institution :
Dept. of Syst. Design Eng., Univ. of Waterloo, Canada
Volume :
33
Issue :
1
fYear :
2003
fDate :
2/1/2003 12:00:00 AM
Firstpage :
122
Lastpage :
132
Abstract :
This paper tackles the issue of bandwidth allocation in asynchronous transfer mode (ATM) networks using recently developed tools of computational intelligence. The efficient bandwidth allocation technique implies effective resources utilization of the network. The fluid flow model has been used effectively among other conventional techniques to estimate the bandwidth for a set of connections. However, such methods have been proven to be inefficient at times in coping with varying and conflicting bandwidth requirements of the different services in ATM networks. This inefficiency is due to the computational complexity of the model. To overcome this difficulty, many approximation-based solutions, such as the fluid flow approximation technique, were introduced. Although such solutions are simple, in terms of computational complexity, they nevertheless suffer from potential inaccuracies in estimating the required bandwidth. Soft computing-based bandwidth controllers, such as neural networks- and neurofuzzy-based controllers, have been shown to effectively solve an indeterminate nonlinear input-output (I-O) relations by learning from examples. Applying these techniques to the bandwidth allocation problem in ATM network yields a flexible control mechanism that offers a fundamental tradeoff for the accuracy-simplicity dilemma.
Keywords :
asynchronous transfer mode; bandwidth allocation; computational complexity; fuzzy logic; fuzzy neural nets; learning by example; quality of service; approximation-based solutions; asynchronous transfer mode networks; bandwidth allocation; computational complexity; computational intelligence; fluid flow model; learning from examples; learning-based resource optimization; neurofuzzy systems; nonlinear input-output relations; soft computing-based bandwidth controllers; Asynchronous transfer mode; Bandwidth; Bit rate; Channel allocation; Communication system traffic control; Computational complexity; Fluid flow; Fluid flow control; Intelligent networks; Quality of service;
fLanguage :
English
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
Publisher :
ieee
ISSN :
1083-4419
Type :
jour
DOI :
10.1109/TSMCB.2003.808178
Filename :
1167359
Link To Document :
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