TELKOMNIKA Telecommunication, Computing, Electronics and Control
Approximation of regression-based fault minimization for network traffic

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Approximation of regression-based fault minimization for network traffic

Approximation, Deep learning, Error minimization, Network traffic, Regression-based prediction

This research associates three distinct approaches for computer network traffic prediction. They are the traditional stochastic gradient descent (SGD) using a few random samplings instead of the complete dataset for each iterative calculation, the gradient descent algorithm (GDA) which is a well-known optimization approach in deep learning, and the proposed method. The network traffic is computed from the traffic load (data and multimedia) of the computer network nodes via the Internet. It is apparent that the SGD is a modest iteration but can conclude suboptimal solutions. The GDA is a complicated one, can function more accurate than the SGD but difficult to manipulate parameters, such as the learning rate, the dataset granularity, and the loss function. Network traffic estimation helps improve performance
and lower costs for various applications, such as an adaptive rate control, load balancing, the quality of service (QoS), fair bandwidth allocation, and anomaly detection. The proposed method confirms optimal values out of parameters using simulation to compute the minimum figure of specified loss function in each iteration.

Chanintorn Jittawiriyanukoon

DOI: 10.12928/TELKOMNIKA.v18i4.13192

Universitas Ahmad Dahlan

August 2020

Sri Wahyuni

ISSN: 1693-6930

http://journal.uad.ac.id/index.php/TELKOMNIKA

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TELKOMNIKA Telecommunication, Computing, Electronics and Control