NDLI: Adaptive measurement-based admission control using neural network predictor

Content Provider	IEEE Xplore Digital Library
Author	Sadek, N. Khotanzad, A. Chen, T.
Copyright Year	2004
Description	Author affiliation: Dept. of Electr. Eng., Southern Methodist Univ., Dallas, TX, USA (Sadek, N.; Khotanzad, A.; Chen, T.)
Abstract	In this paper, we use a neural network based system to predict multi-scale high-speed network traffic and apply it to build a measurement-based admission control (MBAC) scheme. An adaptive two-stage predictor is proposed with the first stage containing a multilayer perceptron (MLP) neural network to forecast multi-step-ahead traffic values at a certain timescale. The second stage combines the forecasts to produce the predicted traffic at higher timescales. Four combination schemes, averaging, RLS, MLP, and fuzzy neural network, are investigated. The performance is tested on four different types of traffic data, MPEG and JPEG video, Ethernet and Internet. The results show that the two-stage predictor outperforms the autoregressive (AR) model. The adaptive MBAC scheme uses a neural network controller to take the decision based on the congestion status and the available bandwidth computed from the multi-scale predicted traffic and the user's peak rate. The proposed scheme achieves higher network utilization compared to the peak rate and Chernoff bound schemes.
Sponsorship	Cisco Syst. General Elec. Ryerson Univ. AVFX Audio Visual Bell Canada Dofasco Dye & Durham Gennum Corp. IEEE Canada Found. Univ. of Toronto Niagara College of Appl. Arts and Technol
Starting Page	859
Ending Page	862
File Size	286169
Page Count	4
File Format	PDF
ISBN	0780382536
ISSN	08407789
DOI	10.1109/CCECE.2004.1345250
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-05-02
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Programmable control Adaptive control Admission control Neural networks Communication system traffic control Traffic control High-speed networks Multilayer perceptrons Multi-layer neural network Resonance light scattering
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Hardware and Architecture

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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