Bandwidth Prediction and Congestion Control for ABR Traffic Based on Neural Networks

Liu, Zhixin; Guan, Xinping; Wu, Huihua

doi:10.1007/11760191_30

Cited by 9 publications

(5 citation statements)

References 5 publications

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“…This section highlights discusses, compares, summarizes and critiques more than eighty research articles on artificial neural network model's application to the diverse area of the economy. The comparison was made based on (i) author(s)/year of publication (ii) ANN modeling (iii) ANN area of application (iii) studied contribution to human challenges (with references to Supplementary Table 2) [ 11 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 , 158 , 159 , 160 , 161 , 162 , 163 , 164 , 165 , 166 , 167 , 168 , 169 , 170 , 171 , 172 , 173 , 174 , 175 , 176 , 177 , 178 , …”

Section: Main Textmentioning

confidence: 99%

State-of-the-art in artificial neural network applications: A survey

Abiodun

Omolara

Dada

et al. 2018

Heliyon

2,160

785

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This is a survey of neural network applications in the real-world scenario. It provides a taxonomy of artificial neural networks (ANNs) and furnish the reader with knowledge of current and emerging trends in ANN applications research and area of focus for researchers. Additionally, the study presents ANN application challenges, contributions, compare performances and critiques methods. The study covers many applications of ANN techniques in various disciplines which include computing, science, engineering, medicine, environmental, agriculture, mining, technology, climate, business, arts, and nanotechnology, etc. The study assesses ANN contributions, compare performances and critiques methods. The study found that neural-network models such as feedforward and feedback propagation artificial neural networks are performing better in its application to human problems. Therefore, we proposed feedforward and feedback propagation ANN models for research focus based on data analysis factors like accuracy, processing speed, latency, fault tolerance, volume, scalability, convergence, and performance. Moreover, we recommend that instead of applying a single method, future research can focus on combining ANN models into one network-wide application.

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Section: Main Textmentioning

confidence: 99%

State-of-the-art in artificial neural network applications: A survey

Abiodun

Omolara

Dada

et al. 2018

Heliyon

2,160

785

View full text Add to dashboard Cite

show abstract

“…Fei et al [6] presented a method to predict the VBR traffic using Fuzzy-neural networks, and Liu et al [7] designed a neural networks predictor which can predict the bursty available bandwidth for ABR traffic. Eswaradass et al [8] proposed an Artificial Neural Network (ANN) based approach for network performance prediction, tested the ANN mechanism on classical trace files and compared its performance with the NWS system.…”

Section: Related Workmentioning

confidence: 99%

Design and Implementation of Bandwidth Prediction Based on Grid Service

Che

2008

2008 10th IEEE International Conference on High Performance Computing and Communications

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Growing complexity in Grid environment makes bandwidth monitoring and prediction both increasingly difficult and increasingly important. The challenge is to design a low-cost and online prediction system to provide Grid users and developers with detailed bandwidth information. Bandwidth prediction is designed based on Grid Service Infrastructure, it is composed of a series of Grid services including monitoring service, prediction service, optimizing service and publication service. Support Vector Regression is used as prediction technology and optimizing strategy isproposed to enhance prediction accuracy. Bandwidth prediction is tested using benchmark data set and experimental results indicate that the prediction service is efficient in optimizing model hyperparameters and prediction error is low on both familiar and unfamiliar bandwidth samples.

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“…From ANN (Artificial Neural Network) to SVM, the models are more and more robust. In [5], [6], it shows ANN is a powerful tool to predict network bandwidth. Although ANN outperforms linear methods, ANN has some disadvantages [7] because it's based on ERM (Empirical Risk Minimization) principle, so it's prone to over-fitting and under-fitting.…”

Section: A Network Bandwidth Predictionmentioning

confidence: 99%