An intelligent traffic prediction framework for 5G network using SDN and fusion learning

2022 ◽  
Author(s):  
K. Tamil Selvi ◽  
R. Thamilselvan
Keyword(s):  
Traffic Prediction ◽  
5G Network

PREDICTING CHARACTERISTICS OF SELF SIMILAR TRAFFIC BY SPLINE-EXTRAPOLATION

2019 ◽  
Author(s):  
Irina Strelkovskay ◽  
Irina Solovskaya ◽  
Anastasija Makoganjuk ◽  
Nikolaj Severin
Keyword(s):  
Quality Characteristics ◽  
Traffic Prediction ◽  
Normative Values ◽  
Spline Functions ◽  
Network Nodes ◽  
Traffic Forecasting ◽  
Network Equipment ◽  
Self Similar ◽  
Accuracy Of Prediction

The problem of forecasting self-similar traffic, which is characterized by a considerable number of ripples and the property of long-term dependence, is considered. It is proposed to use the method of spline extrapolation using linear and cubic splines. The results of self-similar traffic prediction were obtained, which will allow to predict the necessary size of the buffer devices of the network nodes in order to avoid congestion in the network and exceed the normative values ​​of QoS quality characteristics. The solution of the problem of self-similar traffic forecasting obtained with the Simulink software package in Matlab environment is considered. A method of extrapolation based on spline functions is developed. The proposed method has several advantages over the known methods, first of all, it is sufficient ease of implementation, low resource intensity and accuracy of prediction, which can be enhanced by the use of quadratic or cubic interpolation spline functions. Using the method of spline extrapolation, the results of self-similar traffic prediction were obtained, which will allow to predict the required volume of buffer devices, thereby avoiding network congestion and exceeding the normative values ​​of QoS quality characteristics. Given that self-similar traffic is characterized by the presence of "bursts" and a long-term dependence between the moments of receipt of applications in this study, given predetermined data to improve the prediction accuracy, it is possible to use extrapolation based on wavelet functions, the so-called wavelet-extrapolation method. Based on the results of traffic forecasting, taking into account the maximum values ​​of network node traffic, you can give practical guidance on how traffic is redistributed across the network. This will balance the load of network objects and increase the efficiency of network equipment.

Network traffic prediction based on wavelet FARIMA model

2011 ◽  
Vol 31 (4) ◽  
pp. 901-903
Author(s):  
Yong SUN ◽  
Guang-wei BAI ◽  
Lu ZHAO
Keyword(s):  
Network Traffic ◽  
Traffic Prediction

scholarly journals 5G Mobile Services and Scenarios: Challenges and Solutions

2018 ◽  
Vol 10 (10) ◽  
pp. 3626 ◽  
Author(s):  
Yousaf Zikria ◽  
Sung Kim ◽  
Muhammad Afzal ◽  
Haoxiang Wang ◽  
Mubashir Rehmani
Keyword(s):  
Mobile Network ◽  
Future Internet ◽  
Autonomous Driving ◽  
Mobile Services ◽  
Data Traffic ◽  
Fifth Generation ◽  
Wide Range ◽  
Industrial Iot ◽  
5G Network ◽  
Massive Number

The Fifth generation (5G) network is projected to support large amount of data traffic and massive number of wireless connections. Different data traffic has different Quality of Service (QoS) requirements. 5G mobile network aims to address the limitations of previous cellular standards (i.e., 2G/3G/4G) and be a prospective key enabler for future Internet of Things (IoT). 5G networks support a wide range of applications such as smart home, autonomous driving, drone operations, health and mission critical applications, Industrial IoT (IIoT), and entertainment and multimedia. Based on end users’ experience, several 5G services are categorized into immersive 5G services, intelligent 5G services, omnipresent 5G services, autonomous 5G services, and public 5G services. In this paper, we present a brief overview of 5G technical scenarios. We then provide a brief overview of accepted papers in our Special Issue on 5G mobile services and scenarios. Finally, we conclude this paper.

scholarly journals Event-Driven Source Traffic Prediction in Machine-Type Communications Using LSTM Networks

2020 ◽  
Author(s):  
Thulitha Senevirathna ◽  
Bathiya Thennakoon ◽  
Tharindu Sankalpa ◽  
Chatura Seneviratne ◽  
Samad Ali ◽  
...  
Keyword(s):  
Traffic Prediction ◽  
Machine Type ◽  
Machine Type Communications ◽  
Event Driven

Self-supervision Spatiotemporal Part-Whole Convolutional Neural Network for Traffic Prediction

2021 ◽  
pp. 126141
Author(s):  
Linbo Zhai ◽  
Yong Yang ◽  
Shudian Song ◽  
Shuyue Ma ◽  
Xiumin Zhu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Traffic Prediction
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