Nonlinear Autoregressive Recurrent Neural Network Model for Quality of Service Prediction
Yazeed Ahmad Al-Sbou

Yazeed Ahmad Al-Sbou*, Computer Science Department, College of Arts and Science, Applied Science University, Kingdome of Bahrain. Email:

Manuscript received on March 12, 2020. | Revised Manuscript received on March 26, 2020. | Manuscript published on March 30, 2020. | PP: 4762-4770 | Volume-8 Issue-6, March 2020. | Retrieval Number: F9901038620/2020©BEIESP | DOI: 10.35940/ijrte.F9901.038620
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Abstract: Due to the advances in computer networks, Internet and multimedia communications, Quality of Service (QoS) monitoring and assessment become an increasingly important. The importance of assessing QoS stems from the fact it may reflect the resources availability of a network that may provide solutions for QoS provision, routing, monitoring, management … etc. In the literature, several monitoring and measurement approached and methods have been developed to quantify and predict the QoS of multimedia applications transmitted over such networks. In this research, a new QoS prediction system will be designed. The proposed system is based on using the Nonlinear Autoregressive with eXogenous input model (NARX) using recurrent neural network. This prediction system uses in addition to the QoS parameters; previous measured QoS values will used as inputs to this model. The expected output of this new model is the forecasted QoS. The proposed model will be trained, tested, validated and then optimized to provide a good estimate of the QoS provided by the given network. Simulation results are expected to show that the proposed model will have high accurate QoS prediction capabilities compared to other QoS assessment systems adopted in the literature.
Keywords: Computer Networks, QoS, Prediction, Nonlinear Autoregressive Exogenous Inputs (NARX), Multimedia, Videoconferencing.
Scope of the Article: Recent Trends & Developments in Computer Networks.