NDLI: A new wavenet-based network congestion predictor

Content Provider	IEEE Xplore Digital Library
Author	Abdul-Jabbar, J.M. Alwan, M.A. Jasim, A.A.
Copyright Year	2012
Description	Author affiliation: Computer Engineering Department, University of Mosul, Iraq (Abdul-Jabbar, J.M.) \|\| Computer Engineering Department, University of Basrah, Iraq (Alwan, M.A.; Jasim, A.A.)
Abstract	In this paper, a wavelet neural network (WNN) (or wavenet) predictor is used to predict the congestion state for each link in the computer network. The proposed WBCP predictor generates the congestion state for each link based on the utilization values of each link measured in the previous time intervals. WNNs possess the learning and generalization capabilities of the traditional neural networks together with the local characteristics of wavelet functions that enhance network ability to deal with sudden changes and burst network load in efficient manner. The proposed predictor can be used in the context of active congestion control techniques to provide the congestion state of each computer network link.
Starting Page	12
Ending Page	17
File Size	428748
Page Count	6
File Format	PDF
ISBN	9781467302616
e-ISBN	9781467302609
e-ISBN	9781467302593
DOI	10.1109/ICFCN.2012.6206856
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-04-02
Publisher Place	Iraq
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer Networks Wavelet-Neural Networks Prediction Vectors Topology Congestion control Delay Training WBCP Network topology Wavelet Analysis Neural networks Computer networks
Content Type	Text
Resource Type	Article

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