Required Bandwidth Capacity Estimation Scheme For Improved Internet Service Delivery: A Machine Learning Approach

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dc.contributor.authorOdim, Mba
dc.date.accessioned2022-03-01T10:01:56Z
dc.date.available2022-03-01T10:01:56Z
dc.date.issued2019
dc.description.abstractThis paper proposed a data driven, machine learning traffic modelling approach for estimating required bandwidth during telecommunication planning for good quality service delivery. The multilayer perceptron was employed to estimate the offered traffic, a safety factor was incorporated to ensure smooth flow of traffic and a neutralisation factor for moderating under or over provisioning of the bandwidth resource. The offered traffic input lags were varied from 1 to 24. The training epoch values of 200, 500, and 1000 on one and two hidden layered networks were used. The learning algorithm was backpropagation with 0.1 learning rate and 0.9 momentum on logistic sigmoid activation function. The scheme was implemented in Visual Basic and compared with four existing statistically based bandwidth estimation formulae, using four categories of classified traffic of a residential network of a firm in Nigeria. The findings revealed that the proposed scheme gave the minimum cost function, loss rate, and the highest average utilisation on two of the traffic categories (the HOURLY_IN and of HOURLY_OUT), outperformed two of the existing models on the DAILY_IN traffic category and one of the existing models on the DAILY_OUT traffic set. The study recommended that the proposed scheme would serve more effectively toward enhancing internet management related tasks such as general resource capacity planning.en_US
dc.identifier.urihttp://dspace.run.edu.ng:8080/jspui/handle/123456789/1520
dc.language.isoenen_US
dc.subjectBandwidth Estimationen_US
dc.subjectInternet Serviceen_US
dc.subjectMachine learningen_US
dc.subjectTraffic forecastingen_US
dc.subjectMultilayer Perceptronen_US
dc.subjectSafety marginen_US
dc.subjectNeutralization factoren_US
dc.titleRequired Bandwidth Capacity Estimation Scheme For Improved Internet Service Delivery: A Machine Learning Approachen_US
dc.typeArticleen_US
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