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Article
Bandwidth Utilization Prediction in LAN Network Using Time Series Modeling

Authors: Shatha M. Hasan --- Mouayad A. Sahib, --- Ammar T. Namel
Journal: IRAQI JOURNAL OF COMPUTERS,COMMUNICATION AND CONTROL & SYSTEMS ENGINEERING المجلة العراقية لهندسة الحاسبات والاتصالات والسيطرة والنظم ISSN: 18119212 Year: 2019 Volume: 19 Issue: 2 Pages: 78-89
Publisher: University of Technology الجامعة التكنولوجية

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Abstract

monitoring the behavior of computer networks is essential forproblem identification and optimal management. Part of this behavior to bemonitored is the utilization of the network bandwidth. Several techniques areused to model and forecast network traffic such as time series models, moderndata mining techniques, soft computing approaches, and neural networks areused for network traffic analysis and prediction. Efficient bandwidth utilizationand optimization are very interesting research issues in effective networksbecause bandwidth is one of the most required and expensive Internetcomponents needed today. It is generally known that the higher the bandwidthavailable, the better the network performance, thus an essential aid for networkdesign and bandwidth wastage control and a need for trac models which cancapture the characteristics is necessary. In this paper, a time series predictionmodels were proposed for LAN office network bandwidth utilization. Theproposed prediction models are tested by using evaluation metrics used in timeseries such as MSE and performance evaluation plot. Testing results show thatthe proposed models can enhance the detection of bandwidth traffic and providean efficient tool for bandwidth utilization.


Article
SIMULATING AN EVOLUTION STRATEGY TO FORECAST TIME SERIES ARMA(1,1) MODEL

Author: Basa’d Ali Hussain
Journal: Al-Nahrain Journal of Science مجلة النهرين للعلوم ISSN: (print)26635453,(online)26635461 Year: 2007 Volume: 10 Issue: 1 Pages: 167-175
Publisher: Al-Nahrain University جامعة النهرين

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Abstract

This paper presents a multi-member evolution strategy to forecast future value of observed time series model. The proposed method is simple and straight forward and doesn't required any problem specific parameter tuning of the problem. The experiments designed based on simulate for different values of sample size (n=25,50,100),model parameters set ( ) and set to ( ) and use lead time for forecasting future value equal to (l=1,2,3).The value of take equal to (15,100) beside this, there is anther experiment designed for simulating one of method which is known as Box –Jenkins with same values of sample size, model parameters and leads time(l) for number of replicate (RR=1000). Results of this study has cleared by numbers of figures and tables, which are made to clear compression between ES-algorithm and B.J method based on computing values of FMSE (Forecasting Mean Square Error ) & Thiels' (U- statistic) ,statistics used as tools to measures reliability of ES- algorithm and also used to clear accuracy of ES algorithm results. Table(1), tables (2-7) and figures (4 -9) results of statistics show the reliability of algorithm to producing individuals which give reasonably predictions of future values of time series for different values of sample size and lead time values of model parameters.

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