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IJSTR >> Volume 8 - Issue 8, August 2019 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

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

[Full Text]



Mba. O. Odim, Victor C. Osamor



Bandwidth Estimation; Internet Service; Machine learning; Traffic forecasting. Multilayer Perceptron, Safety margin, neutralization factor



This 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.



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