Cellular Network Traffic Prediction Using Exponential Smoothing Methods

Tran, Quang Thanh; Li, Hao; Trinh, Quang Khai

doi:10.32890/jict2019.18.1.1

Cited by 15 publications

(3 citation statements)

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“…Mobile network traffic prediction will be an essential input in to infrastructure planning as well as dynamic and proactive network resource optimization. Extant approaches may have unacceptable accuracy, training times, turnaround times, lack computational complexity and may not therefore account for characteristics such as bursting, non-linear patterns or other important correlations to meet the QoS and QoE requirements of increasingly demanding end users [6,9]. These issues can result in mis-timed resource allocation as well as over-and under-utilization.…”

Section: Discussionmentioning

confidence: 99%

“…There is a well-established literature that focuses on trend, seasonality and anomaly prediction at the network-level and the cell-level to guide mobile network investments and optimization [5,7,8]. Much of the extant research focuses on traffic prediction across that while presenting good forecast results, may have unacceptable training times, turnaround times, lack computational complexity and may not therefore account for characteristics such as bursting, non-linear patterns or other important correlations [6,9].…”

Section: Introductionmentioning

confidence: 99%

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Predicting Short-term Mobile Internet Traffic from Internet Activity using Recurrent Neural Networks

Santos¹,

Rosati²,

Lynn³

et al. 2020

Preprint

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Mobile network traffic prediction is an important input in to network capacity planning and optimization. Existing approaches may lack the speed and computational complexity to account for bursting, non-linear patterns or other important correlations in time series mobile network data. We compare the performance of two deep learning architectures -Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) -for predicting mobile Internet traffic using two months of Telecom Italia data for the metropolitan area of Milan. K-Means clustering was used a priori to group cells based on Internet activity and the Grid Search method was used to identify the best configurations for each model. The predictive quality of the models was evaluated using root mean squared error. Both Deep Learning algorithms were effective in modeling Internet activity and seasonality, both within days and across two months. We find variations in performance across clusters within the city. Overall, the LSTM outperformed the GRU in our experiments.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Short-term Mobile Internet Traffic from Internet Activity using Recurrent Neural Networks

Santos¹,

Rosati²,

Lynn³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The designed method failed to enhance traffic prediction accuracy. An Exponential Smoothing Method was developed in [8] to predict the cellular network traffic with lesser complexity. But the error rate was not reduced.…”

Section: Introductionmentioning

confidence: 99%

ECMCRR-MPDNL for Cellular Network Traffic Prediction With Big Data

et al. 2020

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Big data comprises a large volume of data (i.e., structured and unstructured) stored on a daily basis. Processing such volume of data is a complex task as well as the challenging one. This big data is applied in the cellular network for traffic prediction. Now, benefiting from the big data in cellular networks, it becomes possible to make the analyses one step further into the application level. In order to improve the traffic prediction accuracy with minimum time, Expected Conditional Maximization Clustering and Ruzicka Regression-based Multilayer Perceptron Deep Neural Learning (ECMCRR-MPDNL) technique is introduced. The ECMCRR-MPDNL technique initially collects a large volume of data over the spatial and temporal aspects of cellular networks. Then the collected data are trained with multiple layers such as one input layer, two hidden layers, and one output layer. The activation function is used at the output layer to predict the network traffic based on the similarity value with higher accuracy. These predictors are evaluated using real network traces. Finally, the error rate is calculated for minimizing the prediction error. Experimental evaluation is carried out using a big dataset with different metrics such as prediction accuracy, false-positive and prediction time. The observed result confirms that the proposed ECMCRR-MPDNL technique improves on an average the 98% of performance of network traffic prediction with higher accuracy and 20 % minimum time as well as the false-positive rate as compared to the state-of-the-art methods.

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From statistical‐ to machine learning‐based network traffic prediction

Lohrasbinasab

Shahraki

Taherkordi

et al. 2021

Trans Emerging Tel Tech

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Nowadays, due to the exponential and continuous expansion of new paradigms such as Internet of Things (IoT), Internet of Vehicles (IoV) and 6G, the world is witnessing a tremendous and sharp increase of network traffic. In such large‐scale, heterogeneous, and complex networks, the volume of transferred data, as big data, is considered a challenge causing different networking inefficiencies. To overcome these challenges, various techniques are introduced to monitor the performance of networks, called Network Traffic Monitoring and Analysis (NTMA). Network Traffic Prediction (NTP) is a significant subfield of NTMA which is mainly focused on predicting the future of network load and its behavior. NTP techniques can generally be realized in two ways, that is, statistical‐ and Machine Learning (ML)‐based. In this paper, we provide a study on existing NTP techniques through reviewing, investigating, and classifying the recent relevant works conducted in this field. Additionally, we discuss the challenges and future directions of NTP showing that how ML and statistical techniques can be used to solve challenges of NTP.

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Cellular Network Traffic Prediction Using Exponential Smoothing Methods

Cited by 15 publications

References 0 publications

Predicting Short-term Mobile Internet Traffic from Internet Activity using Recurrent Neural Networks

Predicting Short-term Mobile Internet Traffic from Internet Activity using Recurrent Neural Networks

ECMCRR-MPDNL for Cellular Network Traffic Prediction With Big Data

From statistical‐ to machine learning‐based network traffic prediction

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