Usage of Modern Exponential-Smoothing Models in Network Traffic Modelling

2013 ◽  
pp. 435-444 ◽  
Author(s):  
Roman Jašek ◽  
Anna Szmit ◽  
Maciej Szmit
Keyword(s):  
Network Traffic ◽  
Exponential Smoothing ◽  
Traffic Modelling

scholarly journals CELLULAR NETWORK TRAFFIC PREDICTION USING EXPONENTIAL SMOOTHING METHODS

2018 ◽  
Author(s):  
Quang Thanh Tran ◽  
Li Jun Hao ◽  
Quang Khai Trinh
Keyword(s):  
Network Traffic ◽  
Cellular Network ◽  
Low Cost ◽  
Real Data ◽  
Exponential Smoothing ◽  
High Accuracy ◽  
Training Data ◽  
Traffic Prediction ◽  
Data Traffic ◽  
Smoothing Methods

Wireless traffic prediction plays an important role in network planning and management, especially for real-time decision making and short-term prediction. Systems require high accuracy, low cost, and low computational complexity prediction methods. Although exponential smoothing is an effective method, there is a lack of use with cellular networks and research on data traffic. The accuracy and suitability of this method need to be evaluated using several types of traffic. Thus, this study introduces the application of exponential smoothing as a method of adaptive forecasting of cellular network traffic for cases of voice (in Erlang) and data (in megabytes or gigabytes). Simple and Error, Trend, Seasonal (ETS) methods are used for exponential smoothing. By investigating the effect of their smoothing factors in describing cellular network traffic, the accuracy of forecast using each method is evaluated. This research comprises a comprehensive analysis approach using multiple case study comparisons to determine the best fit model. Different exponential smoothing models are evaluated for various traffic types in different time scales. The experiments are implemented on real data from a commercial cellular network, which is divided into a training data part for modeling and test data part for forecasting comparison. This study found that ETS framework is not suitable for hourly voice traffic, but it provides nearly the same results with Holt–Winter’s multiplicative seasonal (HWMS) in both cases of daily voice and data traffic. HWMS is presumably encompassed by ETC framework and shows good results in all cases of traffic. Therefore, HWMS is recommended for cellular network traffic prediction due to its simplicity and high accuracy.  

scholarly journals Optimising Self-Similarity Network Traffic for Better Performance

2020 ◽  
pp. 164-176
Author(s):  
Ikharo A. B. ◽  
Anyachebelu K. T. ◽  
Blamah N. V. ◽  
Abanihi V. K.
Keyword(s):  
Network Traffic ◽  
Network Performance ◽  
Computer Network ◽  
Ann Model ◽  
Performance Measurements ◽  
Self Similarity ◽  
Traffic Modelling ◽  
Backpropagation Network ◽  
Self Similar

Given the ubiquity of the burstiness present across many networking facilities and services, predicting and managing self-similar traffic has become a key issue owing to new complexities associated with self-similarity which makes difficult the achievement of high network performance and quality of service (QoS). In this study ANN model was used to model and simulate FCE Okene computer network traffic. The ANN is a 2-39-1 Feed Forward Backpropagation network implemented to predict the bursty nature of network traffic. Wireshark tools that measure and capture packets of network traffic was deployed. Moreover, variance-time method is a log-log scale plot, representing variance versus a non-overlapping block of size m aggregate variance level engaged to established conformity of the ANN approach to self-similarity characteristic of the network traffic. The predicted series were then compared with the corresponding real traffic series. Suitable performance measurements used were the Means Square Error (MSE) and the Regression Coefficient. Our results showed that burstiness is present in the network across many time scales. The study also established the characteristic property of a long-range dependence (LRD). The work recommended that network traffic observation should be longer thereby enabling larger volume of traffic to be capture for better accuracy of traffic modelling and prediction.

Linear discriminant analysis in network traffic modelling

2006 ◽  
Vol 19 (1) ◽  
pp. 53-65 ◽  
Author(s):  
Bing-Yi Zhang ◽  
Ya-Min Sun ◽  
Yu-Lan Bian ◽  
Hong-Ke Zhang
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Network Traffic ◽  
Traffic Modelling ◽  
Linear Discriminant

scholarly journals Network Traffic Time Series Performance Analysis Using Statistical Methods

2017 ◽  
Vol 1 (1) ◽  
pp. 1 ◽  
Author(s):  
Purnawansyah Purnawansyah ◽  
Haviluddin Haviluddin ◽  
Rayner Alfred ◽  
Achmad Fanany Onnilita Gaffar
Keyword(s):  
Network Traffic ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Multiplicative Model ◽  
Statistical Techniques ◽  
The Internet ◽  
Autoregressive Integrated Moving Average ◽  
Traffic Characterization ◽  
Traffic Forecasting ◽  
Traffic Time Series

This paper presents an approach for a network traffic characterization by using statistical techniques. These techniques are obtained using the decomposition, winter’s exponential smoothing and autoregressive integrated moving average (ARIMA). In this paper, decomposition and winter’s exponential smoothing techniques were used additive and multiplicative model. Then, ARIMA based-on Box-Jenkins methodology. The results of ARIMA (1,0,2) was shown the best model that can be used to the internet network traffic forecasting.  

scholarly journals Cellular Traffic Prediction Based on an Intelligent Model

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Fawaz Waselallah Alsaade ◽  
Mosleh Hmoud Al-Adhaileh
Keyword(s):  
Network Traffic ◽  
Cellular Network ◽  
Short Term Memory ◽  
Intelligent System ◽  
Exponential Model ◽  
Exponential Smoothing ◽  
Superior Performance ◽  
Network Loading ◽  
Single Exponential ◽  
Cellular Traffic

The evolution of cellular technology development has led to explosive growth in cellular network traffic. Accurate time-series models to predict cellular mobile traffic have become very important for increasing the quality of service (QoS) with a network. The modelling and forecasting of cellular network loading play an important role in achieving the greatest favourable resource allocation by convenient bandwidth provisioning and simultaneously preserve the highest network utilization. The novelty of the proposed research is to develop a model that can help intelligently predict load traffic in a cellular network. In this paper, a model that combines single-exponential smoothing with long short-term memory (SES-LSTM) is proposed to predict cellular traffic. A min-max normalization model was used to scale the network loading. The single-exponential smoothing method was applied to adjust the volumes of network traffic, due to network traffic being very complex and having different forms. The output from a single-exponential model was processed by using an LSTM model to predict the network load. The intelligent system was evaluated by using real cellular network traffic that had been collected in a kaggle dataset. The results of the experiment revealed that the proposed method had superior accuracy, achieving R-square metric values of 88.21%, 92.20%, and 89.81% for three one-month time intervals, respectively. It was observed that the prediction values were very close to the observations. A comparison of the prediction results between the existing LSTM model and our proposed system is presented. The proposed system achieved superior performance for predicting cellular network traffic.

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