Network Traffic Prediction Models for Near- and Long-Term Predictions

2014 ◽  
Author(s):  
Randall Wald ◽  
Taghi M. Khoshgoftaar ◽  
Richard Zuech ◽  
Amri Napolitano
Keyword(s):  
Network Traffic ◽  
Prediction Models ◽  
Traffic Prediction

scholarly journals Prediction of Traffic Generated by IoT Devices Using Statistical Learning Time Series Algorithms

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Shilpa P. Khedkar ◽  
R. Aroul Canessane ◽  
Moslem Lari Najafi
Keyword(s):  
Time Series ◽  
Network Traffic ◽  
Prediction Models ◽  
Forecast Model ◽  
Congestion Management ◽  
Traffic Prediction ◽  
Traffic Forecasting ◽  
Highly Sensitive ◽  
Iot Devices ◽  
Prediction Techniques

An IoT is the communication of sensing devices linked to the Internet in order to communicate data. IoT devices have extremely critical reliability with an efficient and robust network condition. Based on enormous growth in devices and their connectivity, IoT contributes to the bulk of Internet traffic. Prediction of network traffic is very important function of any network. Traffic prediction is important to ensure good system efficiency and ensure service quality of IoT applications, as it relies primarily on congestion management, admission control, allocation of bandwidth to the system, and the identification of anomalies. In this paper, a complete overview of IoT traffic forecasting model using classic time series and artificial neural network is presented. For prediction of IoT traffic, real network traces are used. Prediction models are evaluated using MAE, RMSE, and R -squared values. The experimental results indicate that LSTM- and FNN-based predictive models are highly sensitive and can therefore be used to provide better performance as a timing sequence forecast model than the conventional traffic prediction techniques.

scholarly journals A comprehensive review on hybrid network traffic prediction model

2021 ◽  
Vol 11 (2) ◽  
pp. 1450
Author(s):  
Jinmei Shi ◽  
Yu Beng Leau ◽  
Kun Li ◽  
Joe Henry Obit
Keyword(s):  
Network Traffic ◽  
Prediction Models ◽  
Nonlinear Models ◽  
Past Research ◽  
Fast Response ◽  
Hybrid Network ◽  
Traffic Prediction ◽  
Contributing Factors ◽  
Multi Scale ◽  
Scale Characteristics

Network traffic is a typical nonlinear time series. As such, traditional linear and nonlinear models are inadequate to describe the multi-scale characteristics of traffic, thus compromising the prediction accuracy. Therefore, the research to date has tended to focus on hybrid models rather than the traditional linear and non-linear ones. Generally, a hybrid model adopts two or more methods as combined modelling to analyze and then predict the network traffic. Against this backdrop, this paper will review past research conducted on hybrid network traffic prediction models. The review concludes with a summary of the strengths and limitations of existing hybrid network prediction models which use optimization and decomposition techniques, respectively. These two techniques have been identified as major contributing factors in constructing a more accurate and fast response hybrid network traffic prediction.

scholarly journals NTAM-LSTM models of network traffic prediction

2022 ◽  
Vol 355 ◽  
pp. 02007
Author(s):  
Jihong Zhao ◽  
Xiaoyuan He
Keyword(s):  
Prediction Model ◽  
Network Traffic ◽  
Prediction Models ◽  
Rapid Development ◽  
Attention Mechanism ◽  
Accurate Prediction ◽  
Traffic Prediction ◽  
Network Resources ◽  
Multiple Characteristics ◽  
Lstm Network

Accurate prediction of network traffic is very important in allocating network resources. With the rapid development of network technology, network traffic becomes more complex and diverse. The traditional network traffic prediction model cannot accurately predict the current network traffic within the effective time. This paper proposes a Network Traffic Prediction Model----NTAM-LSTM, which based on Attention Mechanism with Long and Short Time Memory. Firstly, the model preprocesses the historical dataset of network traffic with multiple characteristics. Then the LSTM network is used to make initial prediction for the processed dataset. Finally, attention mechanism is introduced to get more accurate prediction results. Compared with other network traffic prediction models, NTAM-LSTM prediction model can achieve higher prediction accuracy and take shorter running time.

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 Wood creep data collection and unbiased parameter identification of compliance functions

Holzforschung ◽  
2020 ◽  
Vol 74 (11) ◽  
pp. 1011-1020
Author(s):  
Danyang Tong ◽  
Susan Alexis Brown ◽  
David Corr ◽  
Gianluca Cusatis
Keyword(s):  
Parameter Identification ◽  
Prediction Models ◽  
Tall Buildings ◽  
Building Design ◽  
Parameter Distribution ◽  
Experimental Conditions ◽  
Global Emission ◽  
Wood Construction ◽  
Parameter Values

AbstractRising global emission have led to a renewed popularity of timber in building design, including timber-concrete tall buildings up to 18 stories. In spite of this surge in wood construction, there remains a gap in understanding of long-term structural behavior, particularly wood creep. Unlike concrete, code prescriptions for wood design are lacking in robust estimates for structural shortening. Models for wood creep have become increasingly necessary due to the potential for unforeseen shortening, especially with respect to differential shortening. These effects can have serious impacts as timber building heights continue to grow. This study lays the groundwork for wood compliance prediction models for use in timber design. A thorough review of wood creep studies was conducted and viable experimental results were compiled into a database. Studies were chosen based on correlation of experimental conditions with a realistic building environment. An unbiased parameter identification method, originally applied to concrete prediction models, was used to fit multiple compliance functions to each data curve. Based on individual curve fittings, statistical analysis was performed to determine the best fit function and average parameter values for the collective database. A power law trend in wood creep, with lognormal parameter distribution, was confirmed by the results.

scholarly journals Attention based multi-component spatiotemporal cross-domain neural network model for wireless cellular network traffic prediction

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Qingtian Zeng ◽  
Qiang Sun ◽  
Geng Chen ◽  
Hua Duan
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Network Traffic ◽  
Cellular Network ◽  
Critical Issue ◽  
Traffic Prediction ◽  
Weekly Cycle ◽  
Cross Domain ◽  
Cellular Traffic

AbstractWireless cellular traffic prediction is a critical issue for researchers and practitioners in the 5G/B5G field. However, it is very challenging since the wireless cellular traffic usually shows high nonlinearities and complex patterns. Most existing wireless cellular traffic prediction methods lack the abilities of modeling the dynamic spatial–temporal correlations of wireless cellular traffic data, thus cannot yield satisfactory prediction results. In order to improve the accuracy of 5G/B5G cellular network traffic prediction, an attention-based multi-component spatiotemporal cross-domain neural network model (att-MCSTCNet) is proposed, which uses Conv-LSTM or Conv-GRU for neighbor data, daily cycle data, and weekly cycle data modeling, and then assigns different weights to the three kinds of feature data through the attention layer, improves their feature extraction ability, and suppresses the feature information that interferes with the prediction time. Finally, the model is combined with timestamp feature embedding, multiple cross-domain data fusion, and jointly with other models to assist the model in traffic prediction. Experimental results show that compared with the existing models, the prediction performance of the proposed model is better. Among them, the RMSE performance of the att-MCSTCNet (Conv-LSTM) model on Sms, Call, and Internet datasets is improved by 13.70 ~ 54.96%, 10.50 ~ 28.15%, and 35.85 ~ 100.23%, respectively, compared with other existing models. The RMSE performance of the att-MCSTCNet (Conv-GRU) model on Sms, Call, and Internet datasets is about 14.56 ~ 55.82%, 12.24 ~ 29.89%, and 38.79 ~ 103.17% higher than other existing models, respectively.

scholarly journals Autoregressive models of network traffic prediction

2021 ◽  
Vol 1864 (1) ◽  
pp. 012099
Author(s):  
T. Tatarnikova ◽  
B. Sovetov ◽  
V. Chehanovsky
Keyword(s):  
Network Traffic ◽  
Autoregressive Models ◽  
Traffic Prediction
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